PBURC: a patterns-based, unsupervised requirements clustering framework for distributed agile software development

Agile software development methodologies are increasingly adopted by organizations because they focus on the client’s needs, thus safeguarding business value for the final product. At the same time, as the economy and society move toward globalization, more organizations shift to distributed develop...

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Vydáno v:	Requirements engineering Ročník 19; číslo 2; s. 213 - 225
Hlavní autoři:	Belsis, Petros, Koutoumanos, Anastasios, Sgouropoulou, Cleo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.06.2014 Springer Nature B.V
Témata:	Business Clustering Computer programs Computer Science Economics Globalization Optimization Organizations Original Article Requirements analysis Software development Software Engineering Distributed software development Requirements clustering Requirements engineering Agile software process
ISSN:	0947-3602, 1432-010X
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Abstract	Agile software development methodologies are increasingly adopted by organizations because they focus on the client’s needs, thus safeguarding business value for the final product. At the same time, as the economy and society move toward globalization, more organizations shift to distributed development of software projects. From this perspective, while adopting agile techniques seems beneficial, there are still a number of challenges that need to be addressed; among these notable is the effective cooperation between the stakeholders and the geographically distributed development team. In addition, data collection and validation for requirements engineering demands efficient processing techniques in order to handle the volume of data as well as to manage different inconsistencies, when the data are collected using online tools. In this paper, we present “PBURC,” a patterns-based, unsupervised requirements clustering framework, which makes use of machine-learning methods for requirements validation, being able to overcome data inconsistencies and effectively determine appropriate requirements clusters for optimal definition of software development sprints.
AbstractList	Agile software development methodologies are increasingly adopted by organizations because they focus on the client's needs, thus safeguarding business value for the final product. At the same time, as the economy and society move toward globalization, more organizations shift to distributed development of software projects. From this perspective, while adopting agile techniques seems beneficial, there are still a number of challenges that need to be addressed; among these notable is the effective cooperation between the stakeholders and the geographically distributed development team. In addition, data collection and validation for requirements engineering demands efficient processing techniques in order to handle the volume of data as well as to manage different inconsistencies, when the data are collected using online tools. In this paper, we present "PBURC," a patterns-based, unsupervised requirements clustering framework, which makes use of machine-learning methods for requirements validation, being able to overcome data inconsistencies and effectively determine appropriate requirements clusters for optimal definition of software development sprints. Agile software development methodologies are increasingly adopted by organizations because they focus on the client's needs, thus safeguarding business value for the final product. At the same time, as the economy and society move toward globalization, more organizations shift to distributed development of software projects. From this perspective, while adopting agile techniques seems beneficial, there are still a number of challenges that need to be addressed; among these notable is the effective cooperation between the stakeholders and the geographically distributed development team. In addition, data collection and validation for requirements engineering demands efficient processing techniques in order to handle the volume of data as well as to manage different inconsistencies, when the data are collected using online tools. In this paper, we present "PBURC," a patterns-based, unsupervised requirements clustering framework, which makes use of machine-learning methods for requirements validation, being able to overcome data inconsistencies and effectively determine appropriate requirements clusters for optimal definition of software development sprints.[PUBLICATION ABSTRACT]
Author	Belsis, Petros Koutoumanos, Anastasios Sgouropoulou, Cleo
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Cites_doi	10.1109/2.947100 10.1007/978-3-540-25929-9_70 10.1109/ICGSE.2009.27 10.1145/1458082.1458225 10.1145/130069.130129 10.1007/s10664-009-9119-7 10.1109/TIT.1982.1056489 10.1109/RE.2006.17 10.1007/s10515-006-5468-2 10.1109/INMIC.2004.1492915 10.1109/RCIS.2010.5507388 10.1109/MS.2007.60 10.1109/RE.2010.27 10.1016/j.infsof.2010.11.010 10.1109/ICRE.1996.491437 10.1007/BFb0035136 10.1145/1094855.1094961
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References	Kof L (2004) Natural language processing for requirements engineering: applicability to large requirements documents. In: Automated software engineering, proceedings of the workshops, Linz, Austria. Proceedings of the 19th international conference on automated software engineering LeeSYongH-SDistributed agile: project management in a global environmentEmpir Softw Eng20101520421710.1007/s10664-009-9119-7 BalasubramaniamRLanCKannanMPengXCan distributed software development be agile?Commun ACM200610494146 AbrahamssonPSaloORonkainenJWarstaJAgile software development methods: review and analysis2002FinlandTechnical Research Centre of Finland, VTT Publications Duan C, Cleland-Huang J, Mobasher B (2008) A consensus based approach to constrained clustering of software requirements. In: ACM conference on information and knowledge management, NAPA, CA, USA, October 2008, pp 1073–1082 Rolland C, Proix C (1992) A natural language approach for requirements engineering. In: Loucopoulos P (ed) Advanced information systems engineering, LNCS vol. 593. Springer, Verlag, pp 257–277 Meth H, Li Y, Maedche A, Mueller B (2012). Advancing task elicitation systems–an experimental evaluation of design principles. In: Thirty third international conference on information systems, Orlando, 2012, pp 1–20 LloydSPLeast squares quantization in PCMIEEE Trans Inf Theory198228212913710.1109/TIT.1982.10564890504.94015651807 Farmer M (2004) Decision space infrastructure: agile development in a large, distributed team. In: Proceedings of the 2004 agile development conference, IEEE Press, pp 95–99 Turk D, France R, Rumpe B (2002) Limitations of agile software processes. In: Proceedings of the 3rd international conference on eXtreme programming and agile processes in software engineering—XP2002 AmblerSWAgile modeling—effective practices for extreme programming and the unified process2002New YorkWiley DavenportTDeLongDBeersMSuccessful knowledge management projectsSloan Manag Rev19973924357 Li D, Deogun J, Spaulding W, Shuart B (2004). Towards missing data imputation: a study of fuzzy K-means clustering method. In: Tsumoto S, Slowinski R, Komorowski J, Grzymala-Busse JW (eds) (Lecture notes in computer science) vol 3066. Rough sets and current trends in computing (RSCTC 2004).Springer, Sweden, pp 573–579 Racheva Z, Daneva M, Sikkel K, Wieringa R, Herrmann A (2010) Do we know enough about requirements prioritization in agile projects: insight from a case study. In: Proceedings of the 18 IEEE international equirements engineering conference (RE 10), IEEE, Sep 2010, pp 147–156 LaantiMSaloOAbrahamssonPAgile methods rapidly replacing traditional methods at nokia: a survey of opinions on agile transformationInf Softw Technol201153327629010.1016/j.infsof.2010.11.010 WiegersKSoftware requirements20032USAMicrosoft Press Decker B, Ras E, Rech J, Jaubert P, Rieth M (2007) Wiki-based stakeholder participation in requirements engineering. IEEE Software, vol 24, no 2. IEEE press, pp 28–35 HighsmithJCockburnAAgile software development: the business of innovationIEEE Comput200134912012710.1109/2.947100 Rayson P, Garside R, Sawyer P (2000) Assisting requirements engineering with semantic document analysis. In: Proceedings of the RIAO conference, pp 1363–1371 Chen K (2005) An approach to constructing feature models based on requirements clustering. In: 10th international conference on requirements engineering Paris. IEEE Computer Society, France, pp 31–40 Nisar M, Hameed T (2004) Agile methods handling offshore software development issues. In: Proceedings of the 8th international multitopic conference, 2004 (INMIC 2004), pp 417–422 Davis A, Dieste O, Hickey A, Juristo N, Moreno AM (2006) Effectiveness of requirements elicitation techniques: Empirical results derived from a systematic review. In: Proceedings of the 15th international conference on requirements engineering (RE 2006), Minneapolis, MN, USA, pp 176–185 Paasivaara M, Durasiewicz S, Lassenius C (2009) Using scrum in distributed agile development: A multiple case study. In: IEEE international conference on global software engineering, pp 195–204 AmbriolaVGervasiVOn the systematic analysis of natural language requirements with CIRCEAutom Softw Eng2006131107167 Buchholz E, Cyriaks H, Dusterhoft A, Mehlan H, Thalheim B (1995) Applying a natural language dialogue tool for designing databases. In: Proceedings of the first international workshop on applications of natural language to databases, pp 1–16 Racheva Z, Daneva M, Hermann A, Wieringa R (2010) A conceptual model and process for client-driven agile requirements prioritization. A conceptual model and process for client-driven agile requirements prioritization. In: The proceedings of 4th international conference on research challenges in information science (RCIS), Nice, France. IEEE, Los Alamitos Yaung AT (1992) Design and implementation of a requirements clustering analyzer for software system decomposition. In: ACM/SIGAPP symposium on applied computing: technological challenges of the 1990s, Kansas City, pp 1048–1054 GoldinLBerryDAbstFinder, a prototype natural language text abstraction finder for use in requirements elicitationAutom Softw Eng J199741997357412 KotonyaGSomervilleISoftware requirements, processes and techniques, ser. Worldwide series in computer science1998New YorkWiley ZarinahMKSiti SalwahSSupporting collaborative requirements elicitation using focus group discussion techniqueInt J Softw Eng Appl2009335970 Patton J (2005) Finding the forest in the trees. OOPSLA’05, October 16–20, San Diego, California, USA, pp 266–274 Hsia P, Hsu CT, Kung DC, Holder LB (1996) User-Centered system decomposition: Z-Based requirements clustering. In: 2nd international conference on requirements engineering. IEEE Computer Society, Colorado Springs, p 126, Apr 1996 J Highsmith (172_CR11) 2001; 34 G Kotonya (172_CR1) 1998 SW Ambler (172_CR9) 2002 M Laanti (172_CR22) 2011; 53 172_CR18 SP Lloyd (172_CR28) 1982; 28 172_CR17 172_CR19 172_CR14 172_CR13 172_CR16 172_CR15 172_CR10 172_CR31 T Davenport (172_CR30) 1997; 39 S Lee (172_CR32) 2010; 15 P Abrahamsson (172_CR3) 2002 K Wiegers (172_CR2) 2003 172_CR4 172_CR5 172_CR6 172_CR7 172_CR8 L Goldin (172_CR12) 1997; 4 172_CR25 172_CR24 172_CR27 172_CR26 172_CR21 172_CR20 172_CR23 R Balasubramaniam (172_CR29) 2006; 10
References_xml	– reference: AbrahamssonPSaloORonkainenJWarstaJAgile software development methods: review and analysis2002FinlandTechnical Research Centre of Finland, VTT Publications – reference: Chen K (2005) An approach to constructing feature models based on requirements clustering. In: 10th international conference on requirements engineering Paris. IEEE Computer Society, France, pp 31–40 – reference: Buchholz E, Cyriaks H, Dusterhoft A, Mehlan H, Thalheim B (1995) Applying a natural language dialogue tool for designing databases. In: Proceedings of the first international workshop on applications of natural language to databases, pp 1–16 – reference: Nisar M, Hameed T (2004) Agile methods handling offshore software development issues. In: Proceedings of the 8th international multitopic conference, 2004 (INMIC 2004), pp 417–422 – reference: Turk D, France R, Rumpe B (2002) Limitations of agile software processes. In: Proceedings of the 3rd international conference on eXtreme programming and agile processes in software engineering—XP2002 – reference: HighsmithJCockburnAAgile software development: the business of innovationIEEE Comput200134912012710.1109/2.947100 – reference: AmblerSWAgile modeling—effective practices for extreme programming and the unified process2002New YorkWiley – reference: Paasivaara M, Durasiewicz S, Lassenius C (2009) Using scrum in distributed agile development: A multiple case study. In: IEEE international conference on global software engineering, pp 195–204 – reference: Racheva Z, Daneva M, Hermann A, Wieringa R (2010) A conceptual model and process for client-driven agile requirements prioritization. A conceptual model and process for client-driven agile requirements prioritization. In: The proceedings of 4th international conference on research challenges in information science (RCIS), Nice, France. IEEE, Los Alamitos – reference: Duan C, Cleland-Huang J, Mobasher B (2008) A consensus based approach to constrained clustering of software requirements. In: ACM conference on information and knowledge management, NAPA, CA, USA, October 2008, pp 1073–1082 – reference: Hsia P, Hsu CT, Kung DC, Holder LB (1996) User-Centered system decomposition: Z-Based requirements clustering. In: 2nd international conference on requirements engineering. IEEE Computer Society, Colorado Springs, p 126, Apr 1996 – reference: AmbriolaVGervasiVOn the systematic analysis of natural language requirements with CIRCEAutom Softw Eng2006131107167 – reference: Li D, Deogun J, Spaulding W, Shuart B (2004). Towards missing data imputation: a study of fuzzy K-means clustering method. In: Tsumoto S, Slowinski R, Komorowski J, Grzymala-Busse JW (eds) (Lecture notes in computer science) vol 3066. Rough sets and current trends in computing (RSCTC 2004).Springer, Sweden, pp 573–579 – reference: Farmer M (2004) Decision space infrastructure: agile development in a large, distributed team. In: Proceedings of the 2004 agile development conference, IEEE Press, pp 95–99 – reference: DavenportTDeLongDBeersMSuccessful knowledge management projectsSloan Manag Rev19973924357 – reference: ZarinahMKSiti SalwahSSupporting collaborative requirements elicitation using focus group discussion techniqueInt J Softw Eng Appl2009335970 – reference: Kof L (2004) Natural language processing for requirements engineering: applicability to large requirements documents. In: Automated software engineering, proceedings of the workshops, Linz, Austria. Proceedings of the 19th international conference on automated software engineering – reference: LloydSPLeast squares quantization in PCMIEEE Trans Inf Theory198228212913710.1109/TIT.1982.10564890504.94015651807 – reference: KotonyaGSomervilleISoftware requirements, processes and techniques, ser. Worldwide series in computer science1998New YorkWiley – reference: Rolland C, Proix C (1992) A natural language approach for requirements engineering. In: Loucopoulos P (ed) Advanced information systems engineering, LNCS vol. 593. Springer, Verlag, pp 257–277 – reference: LeeSYongH-SDistributed agile: project management in a global environmentEmpir Softw Eng20101520421710.1007/s10664-009-9119-7 – reference: Meth H, Li Y, Maedche A, Mueller B (2012). Advancing task elicitation systems–an experimental evaluation of design principles. In: Thirty third international conference on information systems, Orlando, 2012, pp 1–20 – reference: Patton J (2005) Finding the forest in the trees. OOPSLA’05, October 16–20, San Diego, California, USA, pp 266–274 – reference: Racheva Z, Daneva M, Sikkel K, Wieringa R, Herrmann A (2010) Do we know enough about requirements prioritization in agile projects: insight from a case study. In: Proceedings of the 18 IEEE international equirements engineering conference (RE 10), IEEE, Sep 2010, pp 147–156 – reference: LaantiMSaloOAbrahamssonPAgile methods rapidly replacing traditional methods at nokia: a survey of opinions on agile transformationInf Softw Technol201153327629010.1016/j.infsof.2010.11.010 – reference: GoldinLBerryDAbstFinder, a prototype natural language text abstraction finder for use in requirements elicitationAutom Softw Eng J199741997357412 – reference: Yaung AT (1992) Design and implementation of a requirements clustering analyzer for software system decomposition. In: ACM/SIGAPP symposium on applied computing: technological challenges of the 1990s, Kansas City, pp 1048–1054 – reference: Decker B, Ras E, Rech J, Jaubert P, Rieth M (2007) Wiki-based stakeholder participation in requirements engineering. IEEE Software, vol 24, no 2. IEEE press, pp 28–35 – reference: Rayson P, Garside R, Sawyer P (2000) Assisting requirements engineering with semantic document analysis. In: Proceedings of the RIAO conference, pp 1363–1371 – reference: Davis A, Dieste O, Hickey A, Juristo N, Moreno AM (2006) Effectiveness of requirements elicitation techniques: Empirical results derived from a systematic review. In: Proceedings of the 15th international conference on requirements engineering (RE 2006), Minneapolis, MN, USA, pp 176–185 – reference: WiegersKSoftware requirements20032USAMicrosoft Press – reference: BalasubramaniamRLanCKannanMPengXCan distributed software development be agile?Commun ACM200610494146 – volume: 34 start-page: 120 issue: 9 year: 2001 ident: 172_CR11 publication-title: IEEE Comput doi: 10.1109/2.947100 – ident: 172_CR31 doi: 10.1007/978-3-540-25929-9_70 – ident: 172_CR23 – volume-title: Software requirements, processes and techniques, ser. 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Snippet	Agile software development methodologies are increasingly adopted by organizations because they focus on the client’s needs, thus safeguarding business value... Agile software development methodologies are increasingly adopted by organizations because they focus on the client's needs, thus safeguarding business value...
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SubjectTerms	Business Clustering Computer programs Computer Science Economics Globalization Optimization Organizations Original Article Requirements analysis Software development Software Engineering
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Title	PBURC: a patterns-based, unsupervised requirements clustering framework for distributed agile software development
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