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Knowledge Network for the Development of Software Projects (KnowNetSoft)
Proceedings,16th IFAC Symposium on Proceedings,16th IFAC Symposium on Proceedings,16th IFAC Symposium on Information Control Problems in Manufacturing Proceedings,16th IFAC Symposium on Information Control Problems in Manufacturing Manufacturing Available online at www.sciencedirect.com Proceedings,16th IFAC Symposium on Information Control Problems in Bergamo, Italy, June 11-13, 2018 Information Control Problems in Manufacturing Manufacturing Proceedings,16th IFAC Symposium on Bergamo, Italy, Italy, June 11-13, 2018 Information Control Problems in Bergamo, June 11-13, 2018 Bergamo, June 11-13, Information Control in Manufacturing Bergamo, Italy, Italy, JuneProblems 11-13, 2018 2018 Bergamo, Italy, June 11-13, 2018
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IFAC PapersOnLine 51-11 (2018) 776–781
Knowledge Network for the Development of Software Projects (KnowNetSoft) Knowledge Knowledge Network Network for for the the Development Development of of Software Software Projects Projects (KnowNetSoft) (KnowNetSoft) Patalas-Maliszewska*. Sławomir Kłos.** Knowledge Network forJustyna the Development of Software Projects (KnowNetSoft) Justyna Patalas-Maliszewska*. Sławomir Kłos.**
Justyna Patalas-Maliszewska*. Sławomir Kłos.** Justyna Sławomir Justyna Patalas-Maliszewska*. Patalas-Maliszewska*. Sławomir Kłos.** Kłos.** Patalas-Maliszewska*. Sławomir Kłos.** *Institute of Computer Science andJustyna Production Management, University of Zielona Góra, Zielona Góra, Poland, (e-mail: *Institute of Computer Science and Production [email protected]) Management, University of Zielona Góra, Zielona Góra, Poland, (e-mail: *Institute Management, *Institute of of Computer Computer Science Science and and Production Production [email protected]) Management, University University of of Zielona Zielona Góra, Góra, Zielona Zielona Góra, Góra, Poland, Poland, (e-mail: (e-mail: ** InstituteofofComputer ComputerScience Scienceand andProduction [email protected]) Management,University UniversityofofZielona ZielonaGóra, Góra,Zielona ZielonaGóra, Góra,Poland, Poland,(e-mail: (e-mail: *Institute Management, ** Institute of Computer Science and [email protected]) Management, University of Zielona Góra, Zielona Góra, Poland, (e-mail: [email protected]) [email protected]) ** University ** Institute Institute of of Computer Computer Science Science and and Production Production Management, Management, University of of Zielona Zielona Góra, Góra, Zielona Zielona Góra, Góra, Poland, Poland, (e-mail: (e-mail: [email protected]) ** Institute of Computer Science and Production Management, University of Zielona Góra, Zielona Góra, Poland, (e-mail: [email protected]) [email protected]) [email protected]) Abstract: Tacit knowledge, that is, expert know-how, is highly important in the development of software Abstract: Tacitinfluential knowledge, expertof know-how, highly important in thethe development of software and is highly in that the is, success software is projects. In this paper KnowNetSoft for the Abstract: Tacitinfluential knowledge, that is, expertof know-how, is highly important important in the thethe development of software software Abstract: Tacit knowledge, that is, expert know-how, is highly in development of and is highly in the success software projects. In this paper KnowNetSoft for the effective acquisition, formalisation, storage and classification of knowledge for the development of Abstract: Tacit knowledge, that is, expert know-how, is highly important in the development of software and is highly highly influential in the the success success of software software projects. In In this paper the the KnowNetSoft for the the and is influential in of projects. this paper KnowNetSoft for effective acquisition, formalisation, storage and classification ofidentification knowledge for the development of software projects is constructed. KnowNetSoft model includes (1) of experts as the source and is highly influential in the success of software projects. In this paper the KnowNetSoft for the effective projects acquisition, formalisation, storage and and classification ofidentification knowledge for for the development development of effective acquisition, formalisation, storage classification of knowledge the of software constructed. KnowNetSoft model includes (1) of characteristics experts as the source of knowledge, (2)is acquired and formalised knowledge within an IT company, (3) of the effective acquisition, formalisation, storageknowledge and classification ofIT knowledge for the development of software projects is constructed. KnowNetSoft model includes (1) identification of experts as software projects isacquired constructed. KnowNetSoft model includes (1) identification of characteristics experts as the the source source of knowledge, (2) and formalised within an company, (3) of the software development project, (4) the knowledge base,includes (5) algorithm k-means clustering and Euclidean projects is constructed. KnowNetSoft model (1) identification of experts as the source of knowledge, (2) acquired and formalised knowledge within an IT company, (3) characteristics of the of knowledge, (2) acquired and(4) formalised knowledge within an IT company, (3) characteristics of the software development the knowledge base, algorithm k-means and Euclidean distances. This (2) model isproject, alsoand investigated in knowledge the form of within a(5) web-application and isclustering presented, based on the of knowledge, acquired formalised an IT company, (3) characteristics of the software development project, (4) the base, (5) algorithm k-means clustering and Euclidean software development project, (4) the knowledge knowledge base, (5)web-application algorithm k-means clustering and Euclidean distances. This model is also investigated in the form of a and is presented, based on case study. software development (4) the knowledge base, algorithm k-means Euclidean distances. This also in of and presented, based on distances. This model model is isproject, also investigated investigated in the the form form of aa(5)web-application web-application and is isclustering presented,and based on the the case study. distances. This model is also investigated in the form of a web-application and is presented, based on the case study. case study. © 2018, IFACIntegration (International of Automatic Control) Hosting by Modelling Elsevier Ltd.Framework, All rights reserved. Keywords: of Federation Knowledge/Competence in Enterprise Humancase study. Integration of Knowledge/Competence in Enterprise Modelling Framework, HumanKeywords: Automation Integration, Keywords: Integration of of Knowledge/Competence Knowledge/Competence in in Enterprise Enterprise Modelling Modelling Framework, Framework, HumanHumanKeywords: Integration Automation Integration, Keywords: Integration of Knowledge/Competence in Enterprise Modelling Framework, HumanAutomation Integration, Integration, Automation Automation Integration, collected knowledge of a company (Wang et al., 2014), 1. INTRODUCTION collected knowledge of with a company (Wang al., 2014), (Bocewicz et al., 2015) a network being et a concept for 1. INTRODUCTION collected knowledge of aa company (Wang et al., collected knowledge of company (Wang et al., 2014), 2014), (Bocewicz et al., 2015) with a network being a concept for 1. INTRODUCTION the conceptualisation of knowledge (Guan and Liu, 2016); 1. INTRODUCTION collected knowledge a company (Wang et al., 2014), Tacit knowledge is a very important resource for (Bocewicz et with aa network being aa Liu, concept for et al., al., 2015) 2015) with network being concept for the conceptualisation of knowledge (Guan and 2016); Tacit knowledge 1.isINTRODUCTION a very important resource for (Bocewicz Wang et al., 2014). The KnowNetSoft model includes (1) et al., 2015) with a network being a concept for organisations (Prakash et.a al.,very 2011). Company resource knowledgefor is (Bocewicz the conceptualisation of knowledge (Guan and Liu, 2016); Tacit knowledge is important the conceptualisation of knowledge (Guan and includes Liu, 2016); Tacit knowledge is et.a al.,very important resource for Wang et al., 2014). The KnowNetSoft model (1) organisations (Prakash 2011). Company knowledge is identification of experts as the source of knowledge, (2) the conceptualisation of knowledge (Guan and Liu, 2016); defined as a collection of links between knowledge elements Wang et al., al., 2014). 2014). The as KnowNetSoft model includes (1) (1) Tacit knowledge is et. important resource for organisations (Prakash al., 2011). Company knowledge is et The KnowNetSoft model includes identification of experts the source of an knowledge, (2) organisations (Prakash et. al.,very 2011). Company knowledge is Wang defined a collection ofa links between knowledge elements acquired within IT company, Wang et and al., formalised 2014). The knowledge KnowNetSoft model includes (1) within aas Company (Dibiaggio et al.,Company 2014) and should be identification of experts as the source of knowledge, (2) organisations (Prakash et. al., 2011). knowledge is defined as a collection of links between knowledge elements identification of experts as the source of knowledge, (2) acquired and formalised knowledge within an IT company, defined asCompany a collection of links between knowledge elements within a (Dibiaggio et al., 2014) and should be (3) characteristics of the knowledge software development project, (4) identification of experts as the source of an knowledge, (2) readily available in order that the company’s employees can acquired and formalised within IT company, defined as a collection of links between knowledge elements within a Company (Dibiaggio et al., 2014) and should be acquired and formalised knowledge within an IT company, (3) characteristics of the software development project, (4) within aavailable Company (Dibiaggio et company’s al., 2014) employees and should can be the readily in order that the knowledge base, (5) algorithm k-means clustering and acquired and formalised knowledge within an IT company, carry out the company’s business. The following elements of (3) characteristics of the software development project, (4) within a Company (Dibiaggio et al., 2014) and should be readily available in order that the company’s employees can (3) characteristics of the software development project, (4) the knowledge base, (5) algorithm k-means clustering and readily available in orderbusiness. that the company’s employees can carry out the company’s The following elements of Euclidean distances. This model isdevelopment also investigated in and the (3) characteristics of the software project, (4) company knowledge may be distinguished, viz., the the knowledge base, (5) algorithm k-means clustering readily available in order that the company’s employees can carry out the company’s business. The following elements of the knowledge base, (5) algorithm k-means clustering and Euclidean distances. This model is also investigated in the carry out the company’s may business. The following elements of form company knowledge be distinguished, viz., the of a web-application and is presented, based on theincase the knowledge base, (5) algorithm k-means clustering and employees, reports, internal databases and best practice Euclidean distances. This model is also investigated the carry out the company’s business. The following elements of company knowledge may be be distinguished, viz., the Euclidean distances. This and model is also investigated the form of a web-application is presented, based on thein case company knowledge may distinguished, the employees, reports, databases bestviz., practice Euclidean distances. This and model is also investigated the databases. Firms mayinternal also externaland knowledge from form of is based company knowledge mayabsorb be distinguished, the study. employees, reports, internal databases and bestviz., practice form of aa web-application web-application and is presented, presented, based on on the theincase case study. employees, reports, internal databases and best practice databases. Firms may also absorb external knowledge from form of a web-application and is presented, based on the case customers, Firms along withinternal that absorb from outside suppliers, experts study. employees, reports, databases and best practice databases. may also external knowledge from study. remainder of this paper is organised as follows. Section 2 databases. Firms externalsuppliers, knowledge from The customers, along may with also that absorb from outside experts and institutions. databases. Firms externalsuppliers, knowledge from study. The remainder of this paper is organised as follows. Section 23 customers, along with that from experts presents the theoretical background of the study. Section customers, along may with also that absorb from outside outside suppliers, experts and institutions. The remainder of this this paper paper is organised organised as follows. follows. Section 223 The remainder of is as Section customers, along with that from outside suppliers, experts presents the theoretical background of the study. Section and institutions. describes the theoretical KnowNetSoft model and presents and institutions. remainder of this paper isresearch organised as follows. Sectionthe Software development projects (SD) are defined as creative The presents the background of the study. Section presents the theoretical background of the study. Section describes the KnowNetSoft research model and presents the233 and institutions. Software development projects (SD) aremay defined as creative dataset followed in the case study. Section 4 explains the case presents the theoretical background of the study. Section work for which a single optimal solution not exist (Kraut describes the KnowNetSoft KnowNetSoft research model and presents presents the3 Software development projects (SD) defined as describes the research model and the dataset followed in the caseand study. Section 4 conclusion explains theofcase Software development projects (SD) are aremay defined as creative creative work for which a single optimal solution not exist (Kraut study, discusses the results provides the the describes the KnowNetSoft research model and presents and Streeter, 1995). In order to succeed in software projects, dataset followed in the case study. Section 4 explains the case Software development projects (SD) aremay defined as creative work for aa single optimal solution not (Kraut followed the in the caseand study. Section explains theofcase study, discusses results provides the4 results. conclusion the work for which which single optimal solution may not exist exist (Kraut dataset and Streeter, 1995). In order to succeed in software projects, Sectionthe 5 summarises theSection research dataset followed in the caseand study. 4 conclusion explains theof case those company employees involved in the development of a research. study, discusses results provides the the work for which a single optimal solution may not exist (Kraut and Streeter, 1995). In order to succeed in software projects, study, discusses the results and provides the conclusion of the research. Section 5 summarises the research results. and Streeter, 1995). In order to succeed in software projects, those company employees involved in to the development of a study, discusses the results and provides the conclusion of the software product, require ready access knowledge (Rajlich research. Section 5 summarises the research results. and Streeter, 1995). In order to succeed in software projects, those company company employees involved in to theknowledge development of aa research. Section 5 summarises the research results. those employees involved in the development of software product, require ready access (Rajlich 2. THESection KNOWLEDGE NETWORK OF A COMPANY research. 5 summarises the research results. and Bennett, 2000). Sources of this knowledge are those those company employees involved in the development of a software product, require readyof access to knowledgeare (Rajlich software product, require ready access to knowledge (Rajlich and Bennett, 2000). Sources this knowledge those 2. THE KNOWLEDGE NETWORK OF A COMPANY experts whose availability in the development of are a(Rajlich team’s 2. NETWORK A software product, require ready access to knowledge and Bennett, 2000). Sources of this knowledge those 2. THE THE KNOWLEDGE KNOWLEDGE NETWORK OF A COMPANY COMPANY to Phelps et al. (2012) a OF Knowledge Network and Bennett, 2000). Sources of this knowledge are those According experts whose availability in the development of a team’s 2. THE KNOWLEDGE NETWORK OF A COMPANY software is linked to Sources the performance of a project atteam’s each According and Bennett, 2000). of this knowledge are those to Phelps et al. (2012) a Knowledge Network experts whose availability in the development of a consists of explicit knowledge elements, often defined as experts whose availability in the development of a atteam’s software is linked to the performance of a project each According to Phelps et aa Knowledge Network level of whose that development (Licorish andofMacDonell, 2014); According toexplicit Phelpsknowledge et al. al. (2012) (2012) Knowledge Network experts availability in the development of a team’s consists of elements, often defined as software is linked to the performance a project at each patents, products, or scientific publications (Phelps et al., software is linked to the performance ofMacDonell, a project at2014); each According to Phelps et al. (2012) a Knowledge Network level of that development (Licorish and consists of explicit explicitorknowledge knowledge elements, often often defined as the key research question, therefore, remains as to how such consists of elements, defined as software is linked to the performance of a project at each patents, products, scientific publications (Phelps et al., level of that development (Licorish and MacDonell, 2014); 2012). On otherorhand, we think the needoften for the creation level of research that development (Licorish remains and MacDonell, 2014); consists ofthe explicit knowledge elements, defined as the key question, therefore, as to how such patents, products, scientific publications (Phelps et al., knowledge, acquired from experts and so vital to the patents, products, or scientific publications (Phelps et al., 2012). On the other hand, we think the need for the creation level of research that development (Licorish remains and MacDonell, 2014); the question, therefore, as to such of a Knowledge Network also outlines the importance of the key key research question, therefore, remains as vital to how how such patents, products, or scientific publications (Phelps et al., knowledge, acquired from experts and so to the 2012). On the the other other hand, we we think the need need for the creation creation company’s development teams, can remains be stored in how a readily 2012). On hand, think the the of a Knowledge Network also outlines the for importance of the key research question, therefore, to knowledge, acquired from experts and so to the individually held knowledge and skills (Burt, 2000). Tacit knowledge, acquired from experts andstored soas vital vital to such the 2012). On the other hand, we think the need for the creation company’s development teams, can be in a readily of a Knowledge Network also outlines the importance of available format. So, we are looking for the solution of a Knowledge Network also outlines the importance of individually held knowledge and skills (Burt, 2000). Tacit knowledge, acquired from experts and so vital to the company’s development teams, can be stored stored in aasolution readily knowledge, that is,Network expert know-how, is (Burt, highly important in company’s development teams, can be in readily available format. So, we are looking for the of a Knowledge also outlines the importance of individually held knowledge and skills 2000). Tacit supported SD success from two dimensions: one which individually held knowledge and skills (Burt, 2000). Tacit company’s development teams, can be stored in a readily knowledge, that is, expert know-how, is highly important in available format. So, we we aretwolooking looking for the the solution the development of software and is highly influential in the available format. So, are for solution supported SD success from dimensions: one which individually heldis, knowledge and skills 2000). Tacit knowledge, that expert know-how, is highly important in focusses onformat. the success tacitSo, knowledge and the other which exhibits knowledge, that is, expert know-how, is (Burt, highly important in available we are looking for the solution the development of software and is highly influential in the supported SD from two dimensions: one which of software projects. TheisSD Knowledge Network supported from two dimensions: oneexhibits which success focusses onSD the success tacit knowledge andSD the projects, other which knowledge, that is, expert know-how, is highly important in the development of software and highly influential in the all the characteristics of those previously the development of software and isSD highly influential in the supported SD success from two dimensions: one which success of software projects. The Knowledge Network focusses on the tacit knowledge and the other which exhibits should also provide a “socialand infrastructure” (Aalbers and focusses on the tacit knowledge the projects, other which exhibits the all the characteristics of thoseandSD previously development of software is highly influential in the success of software projects. The SD Knowledge Network completed. of software projects. The SD Knowledge Network focusses on the tacit knowledge the projects, other which exhibits success should also provide a “social infrastructure” (Aalbers and all the of previously Dolfsma, along withThe other elements from the all the characteristics characteristics of those thoseandSD SD projects, previously success of 2015) software projects. SD Knowledge Network completed. should also provide “social infrastructure” (Aalbers and should also provide aa “social infrastructure” (Aalbers and all the characteristics of those SD projects, previously Dolfsma, 2015) along with other elements from the completed. knowledge stock of others (Fleming, 2007). completed. also provide a “social infrastructure” (Aalbers and In this paper, the approach to a Knowledge Network, for the should Dolfsma, 2015) along with other other elements from from the the Dolfsma, with elements knowledge 2015) stock ofalong others (Fleming, 2007). completed. In this paper, the approach to a Knowledge Network, for the 2015) along with other elements from the development of approach softwareto aprojects (KnowNetSoft) is Dolfsma, knowledge stock of others (Fleming, 2007). In this paper, the Knowledge Network, for the knowledge stock of others (Fleming, 2007). are, therefore, looking to construct KnowNetSoft for the In this paper, the to aprojects Knowledge Network, for the development of approach software (KnowNetSoft) is We knowledge stock oflooking others (Fleming, 2007). proposed. Wethe argue that the Network of for a firm We are, therefore, to construct KnowNetSoft for the In this paper, to Knowledge aprojects Knowledge Network, the development of software (KnowNetSoft) is effective acquisition, formalisation, storage and classification development of approach software projects (KnowNetSoft) is We proposed. We argue that the Knowledge Network of a firm are, therefore, looking to construct KnowNetSoft for could take the form of the structural representation of the We are, therefore, looking to construct KnowNetSoft for the the effective acquisition, formalisation, storage and classification development of software projects (KnowNetSoft) is proposed. We argue that the structural Knowledge Network of of aaoffirm firm proposed. the Knowledge Network could takeWe the argue form that of the representation the effective We are, therefore, looking to construct KnowNetSoft for the acquisition, formalisation, storage and classification effective acquisition, formalisation, storage and classification proposed. We argue that the Knowledge Network of a firm could take take the the form form of of the the structural structural representation representation of of the the effective acquisition, formalisation, storage and classification could 2405-8963 © IFAC (International Federation of AutomaticofControl) Copyright © 2018, 2018 IFAC could take the form of the structural representation the 789Hosting by Elsevier Ltd. All rights reserved. Copyright ©under 2018 responsibility IFAC 789Control. Copyright 2018 IFAC 789 Peer review© of International Federation of Automatic Copyright © 2018 2018 IFAC IFAC 789 10.1016/j.ifacol.2018.08.413 Copyright © 789 Copyright © 2018 IFAC 789
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of knowledge for SD. This network should be useful, especially for the management of IT companies, because they need to keep their hold on company knowledge. We agree with Mladkova, that when knowledgeable staff leave a company, their knowledge leaves, along with them (Mladkowa, 2012). For the acquisition of tacit knowledge held by employees, the following methods can be distinguished, namely, verbal analysis, non-verbal analysis, the knowledge audit, the hidden interview, the use of simulators and serious games (Ali and Peebles, 2013); (Govaerts et al., 2013); (Ragsdell et al., 2013); (Schweisfurth and Herstatt, 2016); (Rodriguez et al., 2016); (Boyle et al., 2016). This expert knowledge, thus acquired, should be then formalised and classified. Ontology, that is, the languages currently used in formal ontology, such as, Ontolingua, Flogic, OCML, LOOM, XML – OWL along with semantic web rules, are selected methods of formalising knowledge (Kurtz, 2014). In the classification of knowledge, these can be used when clustering the maximisation algorithm expected, the Principal Component Analysis (PCA), knowledge mapping, the decision tree and regression trees, the Bayesian Network and the Bayesian Classifier, Petri nets (PNs) and the Monte Carlo algorithm (Cornuéjols, 2018); (Nait-Said et al., 2008), (Śliwa and Patalas-Maliszewska, 2016); (Nielsen et al., 2014); (Torres-Jiménez et al, 2015).
Fig. 1. The approach to a KnowNetSoft. Thus, our approach to KnowNetSoft is described in the next section. 3. KNOW-NET-SOFT
The proposed approach to KnowNetSoft consists, therefore, of the following elements:
(1) Identification of knowledge, the experts
(1) Identification of the Company’s sources of knowledge, that is, the experts. (2) A method for the acquisition of knowledge, that being reports on those SD projects which have been completed. (3) The rules and sets of rules which are also a method for the formalisation of knowledge. (4) The knowledge base, according to the characteristics of the software development project. (5) Method for the classification of company held knowledge, including, the algorithm k-means clustering and distance method: Euclidean distances.
the
sources
of
Company
Employees in an IT enterprise, involved in SD projects, can be defined as the knowledge source. For this purpose, the usefulness of personnel (Patalas-Maliszewska, 2013); (Patalas-Maliszewska and Krebs, 2016) has been used to identify experts for SD projects. Based on web-based questionnaires, as completed by each employee, Kn, in an IT company, we are able to obtain the value of his/her personnel usefulness, according to its components, viz. general knowledge, professional knowledge, professional abilities, experience and the capacity for innovation (Patalas and Krebs, 2017). Using an algorithm to test solutions for each employee, it is possible to determine the value of the personnel usefulness of each employee (Patalas-Maliszewska and Krebs, 2016).Then, by using the Fuzzy Analytical Hierarchy Process method (FAHP), the relative dominance of each factor in the personnel usefulness function may be determined for each, Kn employee, the set of experts: SK = {SK1, …, SKn, nϵN} being received thereafter. This element of the KnowNetSoft has been explained in detail in previous research on knowledge workers in a company (PatalasMaliszewska, 2013) (Patalas-Maliszewska and Krebs, 2016). (2) Method for acquiring knowledge: reports of SD projects completed Knowledge about SD projects can be gained from each of the employees selected (SK), by using reports on completed SD projects. A specific format has been developed for this report, according to the defined characteristics of each SD project: 790
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Customer Industry (CI), Customer Size (CS), Goal of the IT Project (GP), Project Location (PL), Project Scope (PS), Work on the Project (WP), Team Work on the project (TW), Communication between the Workers (CW), Project Duration (PD), Methodology (M), Programming (P), Errors and Mistakes (EM), in Customer requirements, Reports (R), Training (T). Each SD project is also evaluated, by the employees selected, as a ‘benchmark of success’ in terms of it having been completed within the defined time (CT), it having been completed within the scope defined (CS) and it having been completed within the prescribed budget (CB). (3) Method for the formalisation of knowledge: rules Each report of completed SD projects received from SK is converted according to the following formula: (1) if there is a "word" in the answer, it is Type 1, (2) if there is no "word" in the answer, it is Type 0, (3) if no data word is in the answer, it is Type 0.5.
Fig. 2. An extract from the web questionnaire for employees, in order to determine the value of personnel usefulness – the professional knowledge component.
This is then stored in the knowledge base according to the characteristics of the software development - (4) element of KnowNetSoft.
The algorithmic solutions implemented for each webknowledge questionnaire enabled the personnel usefulness values for each employee to be obtained.
(5) Method for classifying company knowledge: the algorithm k-means clustering and distance method: Euclidean distances
By using the FAHP method, the weightings of the personnel usefulness values of each employee were assigned. Furthermore, the manager in the IT company evaluates each worker and ultimately assigns expert status to him/her; knowledge about an SD project can then be gleaned from her/him.
Clustering is also a type of unsupervised learning where the goal is the division of objects into groups called ‘clusters’ (Cornuéjols, 2018). In order to define groups, in those IT company projects, included in the knowledge base of SD projects, the algorithm k-means clustering and Euclidean distances methods were used. The next section describes the exemplar functionalities of the KnowNetSoft which implements the above network elements, based on an IT company case study. 4. KNOW-NET-SOFT - A CASE STUDY Below is an extract from the KnowNetSoft, based on the proposed concept (see Fig. 1) and on a real case study from an IT company. According to the first element in KnowNetSoft, webknowledge questionnaires are defined for each employee in an IT company, who realises the process: preparing a new project. Fig 2 presents an extract from the web-questionnaire for employees according to the personnel usefulness function.
Fig. 3. Extract from the web-questionnaire for the manager, in the evaluation of an employee. Reports of SD projects to be completed by the defined experts of an IT company (see Fig. 4):
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Fig. 5. Knowledge base in an IT company. According to the rules as defined, each SD project in the knowledge base is formalised. Knowledge regarding five SD defined projects in an IT company - (see Fig. 6) - is classified using the algorithm - k-means clustering and distance method: Euclidean distance with Statistica ver. 13.3.
Fig. 4. An extract from the report regarding an SD project. Based on reports received from experts, each SD project is stored in the knowledge base according to its characteristics:
It was assumed that two groups of SD projects would be built. Figure 6 presents the research results.
SD = {(CI = Manufacturing Company ˅Service Company ˅Trading), (CS = Small Company ˅ medium Company ˅ large Company), (GP = implementation of an application ˅ creation of a new solution for a customer ˅ improving the existing functionality of solutions in an application ˅ adding current functionality solutions to an application), (PL = head Office ˅ client Office), (PS = CRM ˅Production ˅ Sale ˅ Personnel ˅ Finance ˅ Logistics ˅ Supplies ˅ Warehouse ˅ E-commerce ˅ Business Intelligence ˅ B2B ˅ B2C), (WP= Individual ˅ Team Work), (TW = Only employees within a company ˅ Consultants from the company's ˅ environment ˅ Employees, whose work by customer), (CW = Email ˅ Chat ˅ Direct conversation ˅ Meetings˅ Video-conferencing), (PD = Less than 3 months ˅ 3-6 months ˅ 7-12 months ˅ Over 12 months), (M = The classic methodology ˅ The agile methodology), (P = programming – Language: Java ˅ programming – Language: Java Script ˅ programming – Language: C (C++), (C#) ˅ programming – Language: Objective-C ˅ programming – Language: Python ˅ programming – Language: PHP ˅ programming – Language: (Visual) Basic ˅ programming – Language: Perl ˅ programming – Language: Delphi/Object Pascal ˅ programming – Language: Visual Basic .NET ˅ programming – Language: Assembler ˅ programming – Language: PL/SQL ˅ programming – Language: Swift ˅ programming – Language: MATLAB ˅ programming – Language: Groovy), (EM = mistakes in customer requirements ˅ errors in the application), (R = report about changes in the project ˅ Report about mistakes in the project), (T = The users of the application were trained: in the Head Office ˅in the Client Office ˅ via a network (internet).
Graph of means for continuous variables Number of clusters: 2 k-Means 1,2
Normalized means
1,0
0,8
0,6
0,4
0,2
0,0 CI1
CS3 PL1 PS4 PS9 WP2 CW2 PD2
P1
P6
P11 EM1
the characteristics of SD projects
T2
Cluster 1 Cluster 2
Fig. 6. The SD project groups in an IT company. The members of the first SD projects cluster (CSD1) are: CSD1 = {CI1 - Manufacturing Company), CS1 - Small Company, GP1 - Implementation of an Application, GP3 Improving the existing Functionality of Solutions in an Application, PL1 - Head Office, PS1 - CRM, WP2 - Team Work, TW1 - Employees within a Company, CW1 - Email, PD1 - Less than 3 months, M1 - the Classic Methodology, P2 - Programming – Language: Java Script, EM1 - Mistakes in Customer Requirements, R1 - Report about Changes in the Project, T1 - the Users of the Application were trained in the Head Office}.
Each SD project is also evaluated by selected workers as a ‘benchmark of success’ in terms of it having been completed within the defined time (CT), it having been completed within the defined scope (CS) and it having been completed within the prescribed budget (CB).
The members of the second SD projects cluster are:CSD2 = {CI2 - Service Company, CS1 - Small Company, GP2 Creation of a new Solution for a Customer, PL1 - Head Office, PS3 - Sale, WP2 - Team Work, TW1 - Employees within a Company, CW1 - Email, M1 - the Classic Methodology, P2 - Programming – Language: Java Script, EM2 - Errors in the Application, R1 - Report about Changes in the Project, T1 - the Users of the Application were trained in the Head Office}.
In the IT company presented, the knowledge base, according to the characteristics of the software development, currently includes knowledge of five SD projects (see Fig. 5).
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In an IT company, therefore, knowledge about SD projects should be stored according to the following project characteristics:
Proceedings of the 47th International Conference on System Sciences, IEEE. Boyle, E.A., Hainey, T., Connolly, T. M., Gray, G., Earp, J., Ott, M., Lim, T., Ninaus, M., Ribeiro, C., Pereira, J.(2016). An update to the systematic literature review of empirical evidence of the impacts and outcomes of computer games and serious games. Computers & Education, volume 94, 178-192. Burt, R.S. (2000). The network structure of social capital. Research in Organizational Behavior, volume 22, 345423. Cornuéjols, A., Wemmert, C., Gançarski, P., Bennani, Y. (2018) Collaborative clustering: Why, when, what and how. Information Fusion, volume 39, 81–95. Dibiaggio, L., Nasiriyarb, M., Nestac, L. (2014). Substitutability and complementarity of technological knowledge and the inventive performance of semiconductor companies. Research Policy, volume 43 (9), 1582-1593. Fleming, L., Waguespack, D.M. (2007). Brokerage, boundary spanning, and leadership in open innovation communities, Organization Science, volume 18, 165180. Govaerts, M. J. B,, Van de Wiel, M. W., Schuwirth, L. W. T., Van der Vleuten. C. P. M., Muijtjens, A. M. M. (2013) Workplace-based assessment: raters’ performance theories and constructs. Advances in Health Sciences Education, volume 18(3), 375-396. Guan, J., Liu, N. (2016). Exploitative and exploratory innovations in knowledge network and collaboration network: a patent analysis in the technological field of nano-energy. Research Policy, volume 45, 97-112. Kraut, R.E., Streeter, L.A.. (1995). Coordination in software development. Communications of the ACM, volume 38 (3), 69-81. Kurtz, C., Beaulieu, C.F., Napel, S., Rubin, D.L. (2014) A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations. Journal of Biomedical Informatics, volume 49, 227–244. Licorish, S.A., MacDonell, S.G. (2014). Understanding the attitudes, knowledge sharing behaviors and task performance of core developers: a longitudinal study. Information and Software Technology, volume 56 (12), 1578-1596. Mládková, L. (2012). Management of Knowledge Workers. Wolters Kluwer Business, Bratislava. Nait-Said, R,. Zidani, F., Ouzraoui, N. (2008) Fuzzy risk graph model for determining safety integrity level. International Journal of Quality, Statistics, and Reliability, volume 2008. Nielsen, I., Dang, Q.-V., Nielsen, P., Pawlewski, P. (2014). Scheduling of mobile robots with preemptive tasks. Advances in Intelligent Systems and Computing, volume 290, 19-27. Patalas-Maliszewska, J. (2013). Knowledge Worker Management: Value Assessment, Methods, and Application Tools. Springer, Heidelberg Germany. Patalas-Maliszewska, J., Krebs, I. (2016). A model of the Tacit Knowledge Transfer Support Tool: CKnow-board.
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Customer: CSD1 - Small Manufacturing Company, CSD2 – Small Service Company. • The scope: CSD1 - Implementation of an Application: CRM and improving the existing functionality of solutions in an application, CSD2 - creation of a new solution for a customer: sale. • Realisation: CSD1 and CSD2 - Head Office, Team Work, according to the Classic Methodology, Programming – Language: Java Script. • Communication: CSD1 and CSD2 – Email. • Time: CSD1 and CSD2 - Less than 3 months. • Problems: CSD1 - Mistakes in Customer Requirements, CSD2 - Errors in the Application. • Training of Users: CSD1 and CSD2 - in the Head Office. This KnowNetSoft element: classification of knowledge about SD projects is useful, both for the purpose of preparing an offer for the client- to implement a new project- as well as for the employees of the company, involved in the implementation of an SD project in the search for knowledge related to similar projects already made in the company. 5. CONCLUSIONS Our approach to KnowNetSoft, as presented and based on the case study, provides an opportunity to acquire and convert useful tacit knowledge for the development of software projects. This study tries to ascertain what knowledge about software development projects, as stored within a company, should be used in order to realise a new project within a company and in order to achieve a successful outcome for the project. Bork and Fill (2014) distinguish the following enterprise modelling methods based on the formalization of the processes: ARIS framework, Business Process Management Systems (BPMS), HORUS, Semantic Object Model - SOM, TOVE and Unified Modeling Language – UML (Bork and Fill, 2014). In our further works we are planning to apply one of the methods for conceptualizing our approach to a KnowNetSoft. REFERENCES Aalbers, R., Dolfsma, W. (2015). Innovation Networks: Managing the Networked Organization. Taylor and Francis, Hoboken. Ali, N., Peebles, D. (2013) Reactivity effects of concurrent verbalisation during a graph comprehension task. The annual meeting of the Cognitive Science Society, 17201725. Bocewicz G., Muszyński, W., Banaszak, Z. (2015). Models of multimodal networks and transport processes. Bulletin of the Polish Academy of Sciences, Technical Sciences, volume 63 (3), 635-650. Bork D., Fil H.G. (2014). Formal Aspects of Enterprise Modeling Methods: A Comparison Framework.
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In: Dregvaite, G., Damasevisius, R. (eds.), Information and software technologies: 22nd international conference, ICIST 2016, issue 639, 30-41. Cham Heidelberg: Springer International Publishing Switzerland, Communications in Computer and Information Science. Patalas-Maliszewska, J., Krebs, I. (2017). Modelling an Application for Tacit Knowledge Acquisition Support for an IT Company. Trends und Communities der Rechtsinformatik. IRIS 2017 : Tagungsband des 20. Internationalen Rechtsinformatik Symposions, 275-282. Salzburg, Austria. Phelps, C., Heidl, R., Wadhwa, A. (2012). Knowledge, networks, and knowledge networks: a review and research agenda. Journal of Management, volume 38, 1115-1166. Prakash, A.,Chan, Felix T.S., Deshmukh, S.G., (2011). FMS scheduling with knowledge based genetic algorithm approach, Expert Systems with Applications, volume 38, 3161–3171. Ragsdell, G., Probets, S., Ahmed, G., Murray, I. (2013) Knowledge audit: findings from a case study in the energy sector. In Janiunaite, B.,Petraite, M. (ed.), Proceedings of the 14th European Conference on Knowledge Management, 584-593. Kaunus University of Technology, Lithuania. Rajlich, V.T., Bennett, K.H. (2000). A staged model for the software life cycle. Computer, volume 33 (7), 66-71. Rodriguez, J., Freeman, P. T., Wagner, J., Bridges, W., Pidgeon, P., Alexander, K. (2016) Automotive steering system preferences evaluated using a driving simulator. International Journal of Automotive Technology, volume 17(1), 71-81. Schweisfurth, T. G., Herstatt, C. (2016) How internal users contribute to corporate product innovation: the case of embedded users. RADMA and John Wiley & Sons Ltd. Śliwa, M., Patalas-Maliszewska, J. (2016) A strategic knowledge map for the research and development department in a manufacturing company. Foundations of Management, volume 8. Torres-Jiménez, M., García-Alonso, C.R., Salvador-Carulla, L., Fernández-Rodríguez, V. (2015) Evaluation of system efficiency using the Monte Carlo DEA: The case of small health areas. European Journal of Operational Research, volume 242(1), 525–535. Wang, C., Rodan, S., Fruin, M., Xu, X. (2014). Knowledge networks, collaboration networks, and exploratory innovation. Academy of Management Journal, volume 57, 484-514.
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Knowledge Network for the Development of Software Projects (KnowNetSoft) Knowledge Knowledge Network Network for for the the Development Development of of Software Software Projects Projects (KnowNetSoft) (KnowNetSoft) Patalas-Maliszewska*. Sławomir Kłos.** Knowledge Network forJustyna the Development of Software Projects (KnowNetSoft) Justyna Patalas-Maliszewska*. Sławomir Kłos.**
Justyna Patalas-Maliszewska*. Sławomir Kłos.** Justyna Sławomir Justyna Patalas-Maliszewska*. Patalas-Maliszewska*. Sławomir Kłos.** Kłos.** Patalas-Maliszewska*. Sławomir Kłos.** *Institute of Computer Science andJustyna Production Management, University of Zielona Góra, Zielona Góra, Poland, (e-mail: *Institute of Computer Science and Production [email protected]) Management, University of Zielona Góra, Zielona Góra, Poland, (e-mail: *Institute Management, *Institute of of Computer Computer Science Science and and Production Production [email protected]) Management, University University of of Zielona Zielona Góra, Góra, Zielona Zielona Góra, Góra, Poland, Poland, (e-mail: (e-mail: ** InstituteofofComputer ComputerScience Scienceand andProduction [email protected]) Management,University UniversityofofZielona ZielonaGóra, Góra,Zielona ZielonaGóra, Góra,Poland, Poland,(e-mail: (e-mail: *Institute Management, ** Institute of Computer Science and [email protected]) Management, University of Zielona Góra, Zielona Góra, Poland, (e-mail: [email protected]) [email protected]) ** University ** Institute Institute of of Computer Computer Science Science and and Production Production Management, Management, University of of Zielona Zielona Góra, Góra, Zielona Zielona Góra, Góra, Poland, Poland, (e-mail: (e-mail: [email protected]) ** Institute of Computer Science and Production Management, University of Zielona Góra, Zielona Góra, Poland, (e-mail: [email protected]) [email protected]) [email protected]) Abstract: Tacit knowledge, that is, expert know-how, is highly important in the development of software Abstract: Tacitinfluential knowledge, expertof know-how, highly important in thethe development of software and is highly in that the is, success software is projects. In this paper KnowNetSoft for the Abstract: Tacitinfluential knowledge, that is, expertof know-how, is highly important important in the thethe development of software software Abstract: Tacit knowledge, that is, expert know-how, is highly in development of and is highly in the success software projects. In this paper KnowNetSoft for the effective acquisition, formalisation, storage and classification of knowledge for the development of Abstract: Tacit knowledge, that is, expert know-how, is highly important in the development of software and is highly highly influential in the the success success of software software projects. In In this paper the the KnowNetSoft for the the and is influential in of projects. this paper KnowNetSoft for effective acquisition, formalisation, storage and classification ofidentification knowledge for the development of software projects is constructed. KnowNetSoft model includes (1) of experts as the source and is highly influential in the success of software projects. In this paper the KnowNetSoft for the effective projects acquisition, formalisation, storage and and classification ofidentification knowledge for for the development development of effective acquisition, formalisation, storage classification of knowledge the of software constructed. KnowNetSoft model includes (1) of characteristics experts as the source of knowledge, (2)is acquired and formalised knowledge within an IT company, (3) of the effective acquisition, formalisation, storageknowledge and classification ofIT knowledge for the development of software projects is constructed. KnowNetSoft model includes (1) identification of experts as software projects isacquired constructed. KnowNetSoft model includes (1) identification of characteristics experts as the the source source of knowledge, (2) and formalised within an company, (3) of the software development project, (4) the knowledge base,includes (5) algorithm k-means clustering and Euclidean projects is constructed. KnowNetSoft model (1) identification of experts as the source of knowledge, (2) acquired and formalised knowledge within an IT company, (3) characteristics of the of knowledge, (2) acquired and(4) formalised knowledge within an IT company, (3) characteristics of the software development the knowledge base, algorithm k-means and Euclidean distances. This (2) model isproject, alsoand investigated in knowledge the form of within a(5) web-application and isclustering presented, based on the of knowledge, acquired formalised an IT company, (3) characteristics of the software development project, (4) the base, (5) algorithm k-means clustering and Euclidean software development project, (4) the knowledge knowledge base, (5)web-application algorithm k-means clustering and Euclidean distances. This model is also investigated in the form of a and is presented, based on case study. software development (4) the knowledge base, algorithm k-means Euclidean distances. This also in of and presented, based on distances. This model model is isproject, also investigated investigated in the the form form of aa(5)web-application web-application and is isclustering presented,and based on the the case study. distances. This model is also investigated in the form of a web-application and is presented, based on the case study. case study. © 2018, IFACIntegration (International of Automatic Control) Hosting by Modelling Elsevier Ltd.Framework, All rights reserved. Keywords: of Federation Knowledge/Competence in Enterprise Humancase study. Integration of Knowledge/Competence in Enterprise Modelling Framework, HumanKeywords: Automation Integration, Keywords: Integration of of Knowledge/Competence Knowledge/Competence in in Enterprise Enterprise Modelling Modelling Framework, Framework, HumanHumanKeywords: Integration Automation Integration, Keywords: Integration of Knowledge/Competence in Enterprise Modelling Framework, HumanAutomation Integration, Integration, Automation Automation Integration, collected knowledge of a company (Wang et al., 2014), 1. INTRODUCTION collected knowledge of with a company (Wang al., 2014), (Bocewicz et al., 2015) a network being et a concept for 1. INTRODUCTION collected knowledge of aa company (Wang et al., collected knowledge of company (Wang et al., 2014), 2014), (Bocewicz et al., 2015) with a network being a concept for 1. INTRODUCTION the conceptualisation of knowledge (Guan and Liu, 2016); 1. INTRODUCTION collected knowledge a company (Wang et al., 2014), Tacit knowledge is a very important resource for (Bocewicz et with aa network being aa Liu, concept for et al., al., 2015) 2015) with network being concept for the conceptualisation of knowledge (Guan and 2016); Tacit knowledge 1.isINTRODUCTION a very important resource for (Bocewicz Wang et al., 2014). The KnowNetSoft model includes (1) et al., 2015) with a network being a concept for organisations (Prakash et.a al.,very 2011). Company resource knowledgefor is (Bocewicz the conceptualisation of knowledge (Guan and Liu, 2016); Tacit knowledge is important the conceptualisation of knowledge (Guan and includes Liu, 2016); Tacit knowledge is et.a al.,very important resource for Wang et al., 2014). The KnowNetSoft model (1) organisations (Prakash 2011). Company knowledge is identification of experts as the source of knowledge, (2) the conceptualisation of knowledge (Guan and Liu, 2016); defined as a collection of links between knowledge elements Wang et al., al., 2014). 2014). The as KnowNetSoft model includes (1) (1) Tacit knowledge is et. important resource for organisations (Prakash al., 2011). Company knowledge is et The KnowNetSoft model includes identification of experts the source of an knowledge, (2) organisations (Prakash et. al.,very 2011). Company knowledge is Wang defined a collection ofa links between knowledge elements acquired within IT company, Wang et and al., formalised 2014). The knowledge KnowNetSoft model includes (1) within aas Company (Dibiaggio et al.,Company 2014) and should be identification of experts as the source of knowledge, (2) organisations (Prakash et. al., 2011). knowledge is defined as a collection of links between knowledge elements identification of experts as the source of knowledge, (2) acquired and formalised knowledge within an IT company, defined asCompany a collection of links between knowledge elements within a (Dibiaggio et al., 2014) and should be (3) characteristics of the knowledge software development project, (4) identification of experts as the source of an knowledge, (2) readily available in order that the company’s employees can acquired and formalised within IT company, defined as a collection of links between knowledge elements within a Company (Dibiaggio et al., 2014) and should be acquired and formalised knowledge within an IT company, (3) characteristics of the software development project, (4) within aavailable Company (Dibiaggio et company’s al., 2014) employees and should can be the readily in order that the knowledge base, (5) algorithm k-means clustering and acquired and formalised knowledge within an IT company, carry out the company’s business. The following elements of (3) characteristics of the software development project, (4) within a Company (Dibiaggio et al., 2014) and should be readily available in order that the company’s employees can (3) characteristics of the software development project, (4) the knowledge base, (5) algorithm k-means clustering and readily available in orderbusiness. that the company’s employees can carry out the company’s The following elements of Euclidean distances. This model isdevelopment also investigated in and the (3) characteristics of the software project, (4) company knowledge may be distinguished, viz., the the knowledge base, (5) algorithm k-means clustering readily available in order that the company’s employees can carry out the company’s business. The following elements of the knowledge base, (5) algorithm k-means clustering and Euclidean distances. This model is also investigated in the carry out the company’s may business. The following elements of form company knowledge be distinguished, viz., the of a web-application and is presented, based on theincase the knowledge base, (5) algorithm k-means clustering and employees, reports, internal databases and best practice Euclidean distances. This model is also investigated the carry out the company’s business. The following elements of company knowledge may be be distinguished, viz., the Euclidean distances. This and model is also investigated the form of a web-application is presented, based on thein case company knowledge may distinguished, the employees, reports, databases bestviz., practice Euclidean distances. This and model is also investigated the databases. Firms mayinternal also externaland knowledge from form of is based company knowledge mayabsorb be distinguished, the study. employees, reports, internal databases and bestviz., practice form of aa web-application web-application and is presented, presented, based on on the theincase case study. employees, reports, internal databases and best practice databases. Firms may also absorb external knowledge from form of a web-application and is presented, based on the case customers, Firms along withinternal that absorb from outside suppliers, experts study. employees, reports, databases and best practice databases. may also external knowledge from study. remainder of this paper is organised as follows. Section 2 databases. Firms externalsuppliers, knowledge from The customers, along may with also that absorb from outside experts and institutions. databases. Firms externalsuppliers, knowledge from study. The remainder of this paper is organised as follows. Section 23 customers, along with that from experts presents the theoretical background of the study. Section customers, along may with also that absorb from outside outside suppliers, experts and institutions. The remainder of this this paper paper is organised organised as follows. follows. Section 223 The remainder of is as Section customers, along with that from outside suppliers, experts presents the theoretical background of the study. Section and institutions. describes the theoretical KnowNetSoft model and presents and institutions. remainder of this paper isresearch organised as follows. Sectionthe Software development projects (SD) are defined as creative The presents the background of the study. Section presents the theoretical background of the study. Section describes the KnowNetSoft research model and presents the233 and institutions. Software development projects (SD) aremay defined as creative dataset followed in the case study. Section 4 explains the case presents the theoretical background of the study. Section work for which a single optimal solution not exist (Kraut describes the KnowNetSoft KnowNetSoft research model and presents presents the3 Software development projects (SD) defined as describes the research model and the dataset followed in the caseand study. Section 4 conclusion explains theofcase Software development projects (SD) are aremay defined as creative creative work for which a single optimal solution not exist (Kraut study, discusses the results provides the the describes the KnowNetSoft research model and presents and Streeter, 1995). In order to succeed in software projects, dataset followed in the case study. Section 4 explains the case Software development projects (SD) aremay defined as creative work for aa single optimal solution not (Kraut followed the in the caseand study. Section explains theofcase study, discusses results provides the4 results. conclusion the work for which which single optimal solution may not exist exist (Kraut dataset and Streeter, 1995). In order to succeed in software projects, Sectionthe 5 summarises theSection research dataset followed in the caseand study. 4 conclusion explains theof case those company employees involved in the development of a research. study, discusses results provides the the work for which a single optimal solution may not exist (Kraut and Streeter, 1995). In order to succeed in software projects, study, discusses the results and provides the conclusion of the research. Section 5 summarises the research results. and Streeter, 1995). In order to succeed in software projects, those company employees involved in to the development of a study, discusses the results and provides the conclusion of the software product, require ready access knowledge (Rajlich research. Section 5 summarises the research results. and Streeter, 1995). In order to succeed in software projects, those company company employees involved in to theknowledge development of aa research. Section 5 summarises the research results. those employees involved in the development of software product, require ready access (Rajlich 2. THESection KNOWLEDGE NETWORK OF A COMPANY research. 5 summarises the research results. and Bennett, 2000). Sources of this knowledge are those those company employees involved in the development of a software product, require readyof access to knowledgeare (Rajlich software product, require ready access to knowledge (Rajlich and Bennett, 2000). Sources this knowledge those 2. THE KNOWLEDGE NETWORK OF A COMPANY experts whose availability in the development of are a(Rajlich team’s 2. NETWORK A software product, require ready access to knowledge and Bennett, 2000). Sources of this knowledge those 2. THE THE KNOWLEDGE KNOWLEDGE NETWORK OF A COMPANY COMPANY to Phelps et al. (2012) a OF Knowledge Network and Bennett, 2000). Sources of this knowledge are those According experts whose availability in the development of a team’s 2. THE KNOWLEDGE NETWORK OF A COMPANY software is linked to Sources the performance of a project atteam’s each According and Bennett, 2000). of this knowledge are those to Phelps et al. (2012) a Knowledge Network experts whose availability in the development of a consists of explicit knowledge elements, often defined as experts whose availability in the development of a atteam’s software is linked to the performance of a project each According to Phelps et aa Knowledge Network level of whose that development (Licorish andofMacDonell, 2014); According toexplicit Phelpsknowledge et al. al. (2012) (2012) Knowledge Network experts availability in the development of a team’s consists of elements, often defined as software is linked to the performance a project at each patents, products, or scientific publications (Phelps et al., software is linked to the performance ofMacDonell, a project at2014); each According to Phelps et al. (2012) a Knowledge Network level of that development (Licorish and consists of explicit explicitorknowledge knowledge elements, often often defined as the key research question, therefore, remains as to how such consists of elements, defined as software is linked to the performance of a project at each patents, products, scientific publications (Phelps et al., level of that development (Licorish and MacDonell, 2014); 2012). On otherorhand, we think the needoften for the creation level of research that development (Licorish remains and MacDonell, 2014); consists ofthe explicit knowledge elements, defined as the key question, therefore, as to how such patents, products, scientific publications (Phelps et al., knowledge, acquired from experts and so vital to the patents, products, or scientific publications (Phelps et al., 2012). On the other hand, we think the need for the creation level of research that development (Licorish remains and MacDonell, 2014); the question, therefore, as to such of a Knowledge Network also outlines the importance of the key key research question, therefore, remains as vital to how how such patents, products, or scientific publications (Phelps et al., knowledge, acquired from experts and so to the 2012). On the the other other hand, we we think the need need for the creation creation company’s development teams, can remains be stored in how a readily 2012). On hand, think the the of a Knowledge Network also outlines the for importance of the key research question, therefore, to knowledge, acquired from experts and so to the individually held knowledge and skills (Burt, 2000). Tacit knowledge, acquired from experts andstored soas vital vital to such the 2012). On the other hand, we think the need for the creation company’s development teams, can be in a readily of a Knowledge Network also outlines the importance of available format. So, we are looking for the solution of a Knowledge Network also outlines the importance of individually held knowledge and skills (Burt, 2000). Tacit knowledge, acquired from experts and so vital to the company’s development teams, can be stored stored in aasolution readily knowledge, that is,Network expert know-how, is (Burt, highly important in company’s development teams, can be in readily available format. So, we are looking for the of a Knowledge also outlines the importance of individually held knowledge and skills 2000). Tacit supported SD success from two dimensions: one which individually held knowledge and skills (Burt, 2000). Tacit company’s development teams, can be stored in a readily knowledge, that is, expert know-how, is highly important in available format. So, we we aretwolooking looking for the the solution the development of software and is highly influential in the available format. So, are for solution supported SD success from dimensions: one which individually heldis, knowledge and skills 2000). Tacit knowledge, that expert know-how, is highly important in focusses onformat. the success tacitSo, knowledge and the other which exhibits knowledge, that is, expert know-how, is (Burt, highly important in available we are looking for the solution the development of software and is highly influential in the supported SD from two dimensions: one which of software projects. TheisSD Knowledge Network supported from two dimensions: oneexhibits which success focusses onSD the success tacit knowledge andSD the projects, other which knowledge, that is, expert know-how, is highly important in the development of software and highly influential in the all the characteristics of those previously the development of software and isSD highly influential in the supported SD success from two dimensions: one which success of software projects. The Knowledge Network focusses on the tacit knowledge and the other which exhibits should also provide a “socialand infrastructure” (Aalbers and focusses on the tacit knowledge the projects, other which exhibits the all the characteristics of thoseandSD previously development of software is highly influential in the success of software projects. The SD Knowledge Network completed. of software projects. The SD Knowledge Network focusses on the tacit knowledge the projects, other which exhibits success should also provide a “social infrastructure” (Aalbers and all the of previously Dolfsma, along withThe other elements from the all the characteristics characteristics of those thoseandSD SD projects, previously success of 2015) software projects. SD Knowledge Network completed. should also provide “social infrastructure” (Aalbers and should also provide aa “social infrastructure” (Aalbers and all the characteristics of those SD projects, previously Dolfsma, 2015) along with other elements from the completed. knowledge stock of others (Fleming, 2007). completed. also provide a “social infrastructure” (Aalbers and In this paper, the approach to a Knowledge Network, for the should Dolfsma, 2015) along with other other elements from from the the Dolfsma, with elements knowledge 2015) stock ofalong others (Fleming, 2007). completed. In this paper, the approach to a Knowledge Network, for the 2015) along with other elements from the development of approach softwareto aprojects (KnowNetSoft) is Dolfsma, knowledge stock of others (Fleming, 2007). In this paper, the Knowledge Network, for the knowledge stock of others (Fleming, 2007). are, therefore, looking to construct KnowNetSoft for the In this paper, the to aprojects Knowledge Network, for the development of approach software (KnowNetSoft) is We knowledge stock oflooking others (Fleming, 2007). proposed. Wethe argue that the Network of for a firm We are, therefore, to construct KnowNetSoft for the In this paper, to Knowledge aprojects Knowledge Network, the development of software (KnowNetSoft) is effective acquisition, formalisation, storage and classification development of approach software projects (KnowNetSoft) is We proposed. We argue that the Knowledge Network of a firm are, therefore, looking to construct KnowNetSoft for could take the form of the structural representation of the We are, therefore, looking to construct KnowNetSoft for the the effective acquisition, formalisation, storage and classification development of software projects (KnowNetSoft) is proposed. We argue that the structural Knowledge Network of of aaoffirm firm proposed. the Knowledge Network could takeWe the argue form that of the representation the effective We are, therefore, looking to construct KnowNetSoft for the acquisition, formalisation, storage and classification effective acquisition, formalisation, storage and classification proposed. We argue that the Knowledge Network of a firm could take take the the form form of of the the structural structural representation representation of of the the effective acquisition, formalisation, storage and classification could 2405-8963 © IFAC (International Federation of AutomaticofControl) Copyright © 2018, 2018 IFAC could take the form of the structural representation the 789Hosting by Elsevier Ltd. All rights reserved. Copyright ©under 2018 responsibility IFAC 789Control. Copyright 2018 IFAC 789 Peer review© of International Federation of Automatic Copyright © 2018 2018 IFAC IFAC 789 10.1016/j.ifacol.2018.08.413 Copyright © 789 Copyright © 2018 IFAC 789
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of knowledge for SD. This network should be useful, especially for the management of IT companies, because they need to keep their hold on company knowledge. We agree with Mladkova, that when knowledgeable staff leave a company, their knowledge leaves, along with them (Mladkowa, 2012). For the acquisition of tacit knowledge held by employees, the following methods can be distinguished, namely, verbal analysis, non-verbal analysis, the knowledge audit, the hidden interview, the use of simulators and serious games (Ali and Peebles, 2013); (Govaerts et al., 2013); (Ragsdell et al., 2013); (Schweisfurth and Herstatt, 2016); (Rodriguez et al., 2016); (Boyle et al., 2016). This expert knowledge, thus acquired, should be then formalised and classified. Ontology, that is, the languages currently used in formal ontology, such as, Ontolingua, Flogic, OCML, LOOM, XML – OWL along with semantic web rules, are selected methods of formalising knowledge (Kurtz, 2014). In the classification of knowledge, these can be used when clustering the maximisation algorithm expected, the Principal Component Analysis (PCA), knowledge mapping, the decision tree and regression trees, the Bayesian Network and the Bayesian Classifier, Petri nets (PNs) and the Monte Carlo algorithm (Cornuéjols, 2018); (Nait-Said et al., 2008), (Śliwa and Patalas-Maliszewska, 2016); (Nielsen et al., 2014); (Torres-Jiménez et al, 2015).
Fig. 1. The approach to a KnowNetSoft. Thus, our approach to KnowNetSoft is described in the next section. 3. KNOW-NET-SOFT
The proposed approach to KnowNetSoft consists, therefore, of the following elements:
(1) Identification of knowledge, the experts
(1) Identification of the Company’s sources of knowledge, that is, the experts. (2) A method for the acquisition of knowledge, that being reports on those SD projects which have been completed. (3) The rules and sets of rules which are also a method for the formalisation of knowledge. (4) The knowledge base, according to the characteristics of the software development project. (5) Method for the classification of company held knowledge, including, the algorithm k-means clustering and distance method: Euclidean distances.
the
sources
of
Company
Employees in an IT enterprise, involved in SD projects, can be defined as the knowledge source. For this purpose, the usefulness of personnel (Patalas-Maliszewska, 2013); (Patalas-Maliszewska and Krebs, 2016) has been used to identify experts for SD projects. Based on web-based questionnaires, as completed by each employee, Kn, in an IT company, we are able to obtain the value of his/her personnel usefulness, according to its components, viz. general knowledge, professional knowledge, professional abilities, experience and the capacity for innovation (Patalas and Krebs, 2017). Using an algorithm to test solutions for each employee, it is possible to determine the value of the personnel usefulness of each employee (Patalas-Maliszewska and Krebs, 2016).Then, by using the Fuzzy Analytical Hierarchy Process method (FAHP), the relative dominance of each factor in the personnel usefulness function may be determined for each, Kn employee, the set of experts: SK = {SK1, …, SKn, nϵN} being received thereafter. This element of the KnowNetSoft has been explained in detail in previous research on knowledge workers in a company (PatalasMaliszewska, 2013) (Patalas-Maliszewska and Krebs, 2016). (2) Method for acquiring knowledge: reports of SD projects completed Knowledge about SD projects can be gained from each of the employees selected (SK), by using reports on completed SD projects. A specific format has been developed for this report, according to the defined characteristics of each SD project: 790
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Customer Industry (CI), Customer Size (CS), Goal of the IT Project (GP), Project Location (PL), Project Scope (PS), Work on the Project (WP), Team Work on the project (TW), Communication between the Workers (CW), Project Duration (PD), Methodology (M), Programming (P), Errors and Mistakes (EM), in Customer requirements, Reports (R), Training (T). Each SD project is also evaluated, by the employees selected, as a ‘benchmark of success’ in terms of it having been completed within the defined time (CT), it having been completed within the scope defined (CS) and it having been completed within the prescribed budget (CB). (3) Method for the formalisation of knowledge: rules Each report of completed SD projects received from SK is converted according to the following formula: (1) if there is a "word" in the answer, it is Type 1, (2) if there is no "word" in the answer, it is Type 0, (3) if no data word is in the answer, it is Type 0.5.
Fig. 2. An extract from the web questionnaire for employees, in order to determine the value of personnel usefulness – the professional knowledge component.
This is then stored in the knowledge base according to the characteristics of the software development - (4) element of KnowNetSoft.
The algorithmic solutions implemented for each webknowledge questionnaire enabled the personnel usefulness values for each employee to be obtained.
(5) Method for classifying company knowledge: the algorithm k-means clustering and distance method: Euclidean distances
By using the FAHP method, the weightings of the personnel usefulness values of each employee were assigned. Furthermore, the manager in the IT company evaluates each worker and ultimately assigns expert status to him/her; knowledge about an SD project can then be gleaned from her/him.
Clustering is also a type of unsupervised learning where the goal is the division of objects into groups called ‘clusters’ (Cornuéjols, 2018). In order to define groups, in those IT company projects, included in the knowledge base of SD projects, the algorithm k-means clustering and Euclidean distances methods were used. The next section describes the exemplar functionalities of the KnowNetSoft which implements the above network elements, based on an IT company case study. 4. KNOW-NET-SOFT - A CASE STUDY Below is an extract from the KnowNetSoft, based on the proposed concept (see Fig. 1) and on a real case study from an IT company. According to the first element in KnowNetSoft, webknowledge questionnaires are defined for each employee in an IT company, who realises the process: preparing a new project. Fig 2 presents an extract from the web-questionnaire for employees according to the personnel usefulness function.
Fig. 3. Extract from the web-questionnaire for the manager, in the evaluation of an employee. Reports of SD projects to be completed by the defined experts of an IT company (see Fig. 4):
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Fig. 5. Knowledge base in an IT company. According to the rules as defined, each SD project in the knowledge base is formalised. Knowledge regarding five SD defined projects in an IT company - (see Fig. 6) - is classified using the algorithm - k-means clustering and distance method: Euclidean distance with Statistica ver. 13.3.
Fig. 4. An extract from the report regarding an SD project. Based on reports received from experts, each SD project is stored in the knowledge base according to its characteristics:
It was assumed that two groups of SD projects would be built. Figure 6 presents the research results.
SD = {(CI = Manufacturing Company ˅Service Company ˅Trading), (CS = Small Company ˅ medium Company ˅ large Company), (GP = implementation of an application ˅ creation of a new solution for a customer ˅ improving the existing functionality of solutions in an application ˅ adding current functionality solutions to an application), (PL = head Office ˅ client Office), (PS = CRM ˅Production ˅ Sale ˅ Personnel ˅ Finance ˅ Logistics ˅ Supplies ˅ Warehouse ˅ E-commerce ˅ Business Intelligence ˅ B2B ˅ B2C), (WP= Individual ˅ Team Work), (TW = Only employees within a company ˅ Consultants from the company's ˅ environment ˅ Employees, whose work by customer), (CW = Email ˅ Chat ˅ Direct conversation ˅ Meetings˅ Video-conferencing), (PD = Less than 3 months ˅ 3-6 months ˅ 7-12 months ˅ Over 12 months), (M = The classic methodology ˅ The agile methodology), (P = programming – Language: Java ˅ programming – Language: Java Script ˅ programming – Language: C (C++), (C#) ˅ programming – Language: Objective-C ˅ programming – Language: Python ˅ programming – Language: PHP ˅ programming – Language: (Visual) Basic ˅ programming – Language: Perl ˅ programming – Language: Delphi/Object Pascal ˅ programming – Language: Visual Basic .NET ˅ programming – Language: Assembler ˅ programming – Language: PL/SQL ˅ programming – Language: Swift ˅ programming – Language: MATLAB ˅ programming – Language: Groovy), (EM = mistakes in customer requirements ˅ errors in the application), (R = report about changes in the project ˅ Report about mistakes in the project), (T = The users of the application were trained: in the Head Office ˅in the Client Office ˅ via a network (internet).
Graph of means for continuous variables Number of clusters: 2 k-Means 1,2
Normalized means
1,0
0,8
0,6
0,4
0,2
0,0 CI1
CS3 PL1 PS4 PS9 WP2 CW2 PD2
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the characteristics of SD projects
T2
Cluster 1 Cluster 2
Fig. 6. The SD project groups in an IT company. The members of the first SD projects cluster (CSD1) are: CSD1 = {CI1 - Manufacturing Company), CS1 - Small Company, GP1 - Implementation of an Application, GP3 Improving the existing Functionality of Solutions in an Application, PL1 - Head Office, PS1 - CRM, WP2 - Team Work, TW1 - Employees within a Company, CW1 - Email, PD1 - Less than 3 months, M1 - the Classic Methodology, P2 - Programming – Language: Java Script, EM1 - Mistakes in Customer Requirements, R1 - Report about Changes in the Project, T1 - the Users of the Application were trained in the Head Office}.
Each SD project is also evaluated by selected workers as a ‘benchmark of success’ in terms of it having been completed within the defined time (CT), it having been completed within the defined scope (CS) and it having been completed within the prescribed budget (CB).
The members of the second SD projects cluster are:CSD2 = {CI2 - Service Company, CS1 - Small Company, GP2 Creation of a new Solution for a Customer, PL1 - Head Office, PS3 - Sale, WP2 - Team Work, TW1 - Employees within a Company, CW1 - Email, M1 - the Classic Methodology, P2 - Programming – Language: Java Script, EM2 - Errors in the Application, R1 - Report about Changes in the Project, T1 - the Users of the Application were trained in the Head Office}.
In the IT company presented, the knowledge base, according to the characteristics of the software development, currently includes knowledge of five SD projects (see Fig. 5).
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In an IT company, therefore, knowledge about SD projects should be stored according to the following project characteristics:
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Customer: CSD1 - Small Manufacturing Company, CSD2 – Small Service Company. • The scope: CSD1 - Implementation of an Application: CRM and improving the existing functionality of solutions in an application, CSD2 - creation of a new solution for a customer: sale. • Realisation: CSD1 and CSD2 - Head Office, Team Work, according to the Classic Methodology, Programming – Language: Java Script. • Communication: CSD1 and CSD2 – Email. • Time: CSD1 and CSD2 - Less than 3 months. • Problems: CSD1 - Mistakes in Customer Requirements, CSD2 - Errors in the Application. • Training of Users: CSD1 and CSD2 - in the Head Office. This KnowNetSoft element: classification of knowledge about SD projects is useful, both for the purpose of preparing an offer for the client- to implement a new project- as well as for the employees of the company, involved in the implementation of an SD project in the search for knowledge related to similar projects already made in the company. 5. CONCLUSIONS Our approach to KnowNetSoft, as presented and based on the case study, provides an opportunity to acquire and convert useful tacit knowledge for the development of software projects. This study tries to ascertain what knowledge about software development projects, as stored within a company, should be used in order to realise a new project within a company and in order to achieve a successful outcome for the project. Bork and Fill (2014) distinguish the following enterprise modelling methods based on the formalization of the processes: ARIS framework, Business Process Management Systems (BPMS), HORUS, Semantic Object Model - SOM, TOVE and Unified Modeling Language – UML (Bork and Fill, 2014). In our further works we are planning to apply one of the methods for conceptualizing our approach to a KnowNetSoft. REFERENCES Aalbers, R., Dolfsma, W. (2015). Innovation Networks: Managing the Networked Organization. Taylor and Francis, Hoboken. Ali, N., Peebles, D. (2013) Reactivity effects of concurrent verbalisation during a graph comprehension task. The annual meeting of the Cognitive Science Society, 17201725. Bocewicz G., Muszyński, W., Banaszak, Z. (2015). Models of multimodal networks and transport processes. Bulletin of the Polish Academy of Sciences, Technical Sciences, volume 63 (3), 635-650. Bork D., Fil H.G. (2014). Formal Aspects of Enterprise Modeling Methods: A Comparison Framework.
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