The v-algorithm for discovering software process lines
A software company can define a software process line (SPrL) to deal with projects with different characteristics. This entails defining a base process and its variation points; the SPrL is then tailored to each project. This approach avoids the co‐evolution problems but is expensive to set up. In c...
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| Published in | Journal of software : evolution and process Vol. 28; no. 9; pp. 783 - 799 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Chichester
Blackwell Publishing Ltd
01.09.2016
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2047-7473 2047-7481 |
| DOI | 10.1002/smr.1778 |
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| Abstract | A software company can define a software process line (SPrL) to deal with projects with different characteristics. This entails defining a base process and its variation points; the SPrL is then tailored to each project. This approach avoids the co‐evolution problems but is expensive to set up. In companies that register project events, this information could be used to discover the SPrL. However, traditional discovery algorithms focus on extracting a single process, which can be overly complex and would not be useful for managing future projects. Filtering out less frequent behavior leads to the discovery of simpler models, but these may not include relevant behavior. To address these issues, we propose the v‐algorithm, which discovers a SPrL from process logs. Two thresholds split the log into three clusters based on relation frequency. The first one is used to generate the base process, the second one is used to identify variable elements, and the last one is discarded. We used the v‐algorithm to discover the SPrL of Mobius, a small Chilean software company. We also discuss how the values of the thresholds affect the process discovery quality dimensions, extending existing metrics to the SPrL case. Copyright © 2016 John Wiley & Sons, Ltd. |
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| AbstractList | A software company can define a software process line (SPrL) to deal with projects with different characteristics. This entails defining a base process and its variation points; the SPrL is then tailored to each project. This approach avoids the co-evolution problems but is expensive to set up. In companies that register project events, this information could be used to discover the SPrL. However, traditional discovery algorithms focus on extracting a single process, which can be overly complex and would not be useful for managing future projects. Filtering out less frequent behavior leads to the discovery of simpler models, but these may not include relevant behavior. To address these issues, we propose the v-algorithm, which discovers a SPrL from process logs. Two thresholds split the log into three clusters based on relation frequency. The first one is used to generate the base process, the second one is used to identify variable elements, and the last one is discarded. We used the v-algorithm to discover the SPrL of Mobius, a small Chilean software company. We also discuss how the values of the thresholds affect the process discovery quality dimensions, extending existing metrics to the SPrL case. Copyright © 2016 John Wiley & Sons, Ltd. A software company can define a software process line (SPrL) to deal with projects with different characteristics. This entails defining a base process and its variation points; the SPrL is then tailored to each project. This approach avoids the co‐evolution problems but is expensive to set up. In companies that register project events, this information could be used to discover the SPrL. However, traditional discovery algorithms focus on extracting a single process, which can be overly complex and would not be useful for managing future projects. Filtering out less frequent behavior leads to the discovery of simpler models, but these may not include relevant behavior. To address these issues, we propose the v‐algorithm , which discovers a SPrL from process logs. Two thresholds split the log into three clusters based on relation frequency. The first one is used to generate the base process, the second one is used to identify variable elements, and the last one is discarded. We used the v‐algorithm to discover the SPrL of Mobius, a small Chilean software company. We also discuss how the values of the thresholds affect the process discovery quality dimensions, extending existing metrics to the SPrL case. Copyright © 2016 John Wiley & Sons, Ltd. |
| Author | Bastarrica, María Cecilia Rojas Blum, Fabian Simmonds, Jocelyn |
| Author_xml | – sequence: 1 givenname: Fabian surname: Rojas Blum fullname: Rojas Blum, Fabian email: fblum@dcc.uchile.cl, Correspondence to: Fabian Rojas Blum, Computer Science Department (DCC), University of Chile, Santiago, Chile., fblum@dcc.uchile.cl organization: Computer Science Department (DCC), University of Chile, Santiago, Chile – sequence: 2 givenname: Jocelyn surname: Simmonds fullname: Simmonds, Jocelyn organization: Computer Science Department (DCC), University of Chile, Santiago, Chile – sequence: 3 givenname: María Cecilia surname: Bastarrica fullname: Bastarrica, María Cecilia organization: Computer Science Department (DCC), University of Chile, Santiago, Chile |
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| References_xml | – reference: Murata T. Petri nets: Properties, analysis and applications. Proceedings of the IEEE 1989; 77(4):541-580. – reference: Hurtado Alegría JA, Bastarrica MC, Quispe A, Ochoa SF. MDE-based process tailoring strategy. Journal of Software: Evolution and Process 2014; 26(4):386-403. – reference: van der Aalst W. Process Mining. Springer: Berlin Heidelberg, 2011. – reference: van der Aalst W, Weijters A. Process mining: a research agenda. Computers in Industry 2004; 53(3):231-244. – reference: van der Aalst W, van Dongen B, Herbst J, Maruster L, Schimm G, Weijters A. Workflow mining: a survey of issues and approaches. Data & Knowledge Engineering 2003; 47(2):237-267. – reference: Medeiros A, Weijters A, van der Aalst W. Genetic process mining: an experimental evaluation. Data Mining and Knowledge Discovery 2007; 14(2):245-304. – reference: Rozinat A, van der Aalst WMP. Conformance checking of processes based on monitoring real behavior. Information Systems 2008; 33:64-95. – reference: Pillat R, Oliveira T, Alencar P, Cowan D. BPMNt: A BPMN extension for specifying software process tailoring. Information and Software Technology 2015; 57:95-115. – reference: Martínez-Ruiz T, García F, Piattini M, Münch J. Modelling software process variability: an empirical study. IET Software 2011; 5(2):172-187. – reference: van der Aalst W, van Hee K, ter Hofstede A, Sidorova N, Verbeek H, Voorhoeve M, Wynn M. Soundness of workflow nets: classification, decidability, and analysis. Formal Aspects of Computing 2011; 23(3):333-363. – reference: Pohl K, Böckle G, van der Linden FJ. Software Product Line Engineering: Foundations, Principles and Techniques. Springer-Verlag New York, Inc., 2005. – reference: Münch J, Armbrust O, Kowalczyk M, Soto M. Software Process Definition and Management. Springer: Berlin Heidelberg, 2012. – reference: de Medeiros A, Weijters A, van der Aalst W. Genetic process mining: an experimental evaluation. Data Mining and Knowledge Discovery 2007; 14(2):245-304. – reference: van der Aalst W, Weijters T, Maruster L. Workflow mining: discovering process models from event logs. 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| Title | The v-algorithm for discovering software process lines |
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