Fostering Real-Time Software Analysis by Leveraging Heterogeneous and Autonomous Software Repositories
Mining software repositories allow software practitioners to improve the quality of software systems and to support maintenance based on historical data. Such data is scattered across autonomous and heterogeneous information sources, such as version control, bug tracking and build automation systems...
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| Published in | IEICE Transactions on Information and Systems Vol. E101.D; no. 11; pp. 2730 - 2743 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Tokyo
The Institute of Electronics, Information and Communication Engineers
01.11.2018
Japan Science and Technology Agency |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0916-8532 1745-1361 |
| DOI | 10.1587/transinf.2018EDP7094 |
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| Abstract | Mining software repositories allow software practitioners to improve the quality of software systems and to support maintenance based on historical data. Such data is scattered across autonomous and heterogeneous information sources, such as version control, bug tracking and build automation systems. Despite having many tools to track and measure the data originated from such repositories, software practitioners often suffer from a scarcity of the techniques necessary to dynamically leverage software repositories to fulfill their complex information needs. For example, answering a question such as “What is the number of commits between two successful builds?” requires tiresome manual inspection of multiple repositories. As a solution, this paper presents a conceptual framework and a proof of concept visual query interface to satisfy distinct software quality related information needs of software practitioners. The data originated from repositories is integrated and analyzed to perform systematic investigations, which helps to uncover hidden relationships between software quality and trends of software evolution. This approach has several significant benefits such as the ability to perform real-time analyses, the ability to combine data from various software repositories and generate queries dynamically. The framework evaluated with 31 subjects by using a series of questions categorized into three software evolution scenarios. The evaluation results evidently show that our framework surpasses the state of the art tools in terms of correctness, time and usability. |
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| AbstractList | Mining software repositories allow software practitioners to improve the quality of software systems and to support maintenance based on historical data. Such data is scattered across autonomous and heterogeneous information sources, such as version control, bug tracking and build automation systems. Despite having many tools to track and measure the data originated from such repositories, software practitioners often suffer from a scarcity of the techniques necessary to dynamically leverage software repositories to fulfill their complex information needs. For example, answering a question such as “What is the number of commits between two successful builds?” requires tiresome manual inspection of multiple repositories. As a solution, this paper presents a conceptual framework and a proof of concept visual query interface to satisfy distinct software quality related information needs of software practitioners. The data originated from repositories is integrated and analyzed to perform systematic investigations, which helps to uncover hidden relationships between software quality and trends of software evolution. This approach has several significant benefits such as the ability to perform real-time analyses, the ability to combine data from various software repositories and generate queries dynamically. The framework evaluated with 31 subjects by using a series of questions categorized into three software evolution scenarios. The evaluation results evidently show that our framework surpasses the state of the art tools in terms of correctness, time and usability. |
| Author | WIMALARATNE, Prasad WIJESIRIWARDANA, Chaman |
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Wijesiriwardana, G. Ghezzi, and H. Gall, “A guided mashup framework for rapid software analysis service composition,” 2012 19th Asia-Pacific Software Engineering Conference (APSEC), pp.725-728, IEEE, 2012. 10.1109/apsec.2012.112 [31] J. Brooke et al., “Sus-a quick and dirty usability scale,” Usability evaluation in Industry, vol.189, no.194, pp.4-7, 1996. [30] C. Wohlin, P. Runeson, M. Höst, M.C. Ohlsson, B. Regnell, and A. Wesslén, Experimentation in Software Engineering, Springer Science & Business Media, 2012. [19] M. DAmbros, H. Gall, M. Lanza, and M. Pinzger, “Analysing software repositories to understand software evolution,” Software Evolution, pp.37-67, Springer, 2008. [34] G. Gousios and D. Spinellis, “Conducting quantitative software engineering studies with alitheia core,” Empirical Software Engineering, vol.19, no.4, pp.885-925, 2014. 10.1007/s10664-013-9242-3 [1] H. Hemmati, S. Nadi, O. Baysal, O. Kononenko, W. Wang, R. Holmes, and M.W. Godfrey, “The msr cookbook: Mining a decade of research,” 2013 10th IEEE Working Conference on Mining Software Repositories (MSR), pp.343-352, IEEE, 2013. 10.1109/msr.2013.6624048 [3] A.E. Hassan, “The road ahead for mining software repositories,” Frontiers of Software Maintenance, 2008. FoSM 2008., pp.48-57, IEEE, 2008. 10.1109/fosm.2008.4659248 [7] B. De Alwis and G.C. Murphy, “Answering conceptual queries with ferret,” ACM/IEEE 30th International Conference on Software Engineering, 2008. ICSE'08. pp.21-30, IEEE, 2008. 10.1145/1368088.1368092 [9] T.D. LaToza, G. Venolia, and R. DeLine, “Maintaining mental models: a study of developer work habits,” Proc. 28th international conference on Software engineering, pp.492-501, ACM, 2006. 10.1145/1134285.1134355 [16] M. Brandtner, S.C. Muller, P. Leitner, and H.C. Gall, “Sqa-profiles: Rule-based activity profiles for continuous integration environments,” 2015 IEEE 22nd International Conference on Software Analysis, Evolution and Reengineering (SANER), pp.301-310, IEEE, 2015. 10.1109/saner.2015.7081840 [26] Y. Kanellopoulos, P. Antonellis, D. Antoniou, C. Makris, E. Theodoridis, C. Tjortjis, and N. Tsirakis, “Code quality evaluation methodology using the ISO/IEC 9126 standard,” arXiv preprint arXiv:1007.5117, 2010. [33] G. Robles, J.M. González-Barahona, C. Cervigón, A. Capiluppi, and D. Izquierdo-Cortázar, “Estimating development effort in free/open source software projects by mining software repositories: a case study of openstack,” Proc. 11th Working Conference on Mining Software Repositories, pp.222-231, ACM, 2014. 10.1145/2597073.2597107 [8] A.J. Ko, R. DeLine, and G. Venolia, “Information needs in collocated software development teams,” Proc. 29th international conference on Software Engineering, pp.344-353, IEEE Computer Society, 2007. 10.1109/icse.2007.45 [28] B. Vasilescu, A. Serebrenik, and M. van den Brand, “Comparative study of software metrics aggregation techniques,” Proc. International Worskhop Benevol, vol.2010, 2010. [35] H. Kagdi, M.L. Collard, and J.I. Maletic, “A survey and taxonomy of approaches for mining software repositories in the context of software evolution,” Journal of Software Maintenance and Evolution: Research and Practice, vol.19, no.2, pp.77-131, 2007. 10.1002/smr.344 [13] T. Fritz and G.C. Murphy, “Using information fragments to answer the questions developers ask,” Proc. 32nd ACM/IEEE International Conference on Software Engineering, vol.1, pp.175-184, ACM, 2010. 10.1145/1806799.1806828 [10] T.D. LaToza and B.A. Myers, “Hard-to-answer questions about code,” Evaluation and Usability of Programming Languages and Tools, p.8, ACM, 2010. [14] Y.-F. Li and H. Zhang, “Integrating software engineering data using semantic web technologies,” Proc. 8th Working Conference on Mining Software Repositories, pp.211-214, ACM, 2011. 10.1145/1985441.1985473 [24] ISO:IEC25010, “Systems and software engineering systems and software quality requirements and evaluation (square) system and software quality models,” International Organisation for Standardisation, Geneva, Switzerland, 2011. [25] ISO:IEC9126-1, “Software engineering-product quality-part 1: Quality model,” International Organization for Standardization, Geneva, Switzerland, 2001. [22] M. Rebouças, R.O. Santos, G. Pinto, and F. Castor, “How does contributors' involvement influence the build status of an open-source software project?,” Proc. 14th International Conference on Mining Software Repositories, pp.475-478, IEEE Press, 2017. 10.1109/msr.2017.32 [4] Y. Sakamoto, S. Matsumoto, S. Saiki, and M. Nakamura, “Visualizing software metrics with service-oriented mining software repository for reviewing personal process,” 2013 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), pp.549-554, IEEE, 2013. 10.1109/snpd.2013.96 [21] D. Yang, P. Martins, V. Saini, and C. Lopes, “Stack overflow in github: any snippets there?,” Proc. 14th International Conference on Mining Software Repositories, pp.280-290, IEEE Press, 2017. 10.1109/msr.2017.13 [2] T. Roehm, R. Tiarks, R. Koschke, and W. Maalej, “How do professional developers comprehend software?,” Proc. 34th International Conference on Software Engineering, pp.255-265, IEEE Press, 2012. 10.1109/icse.2012.6227188 [5] V.R. Basili, G. Caldiera, H.D. Rombach, and R. van Solingen, “Goal question metric (GQM) approach,” ed. J. Marciniak, Encyclopedia of Software Engineering, vol.1, pp.578-583, Wiley Online Library, 2002. 22 23 24 25 26 27 28 29 30 31 10 32 11 33 12 34 13 35 14 15 16 17 18 19 1 2 3 4 5 6 7 8 9 20 21 |
| References_xml | – reference: [31] J. Brooke et al., “Sus-a quick and dirty usability scale,” Usability evaluation in Industry, vol.189, no.194, pp.4-7, 1996. – reference: [35] H. Kagdi, M.L. Collard, and J.I. Maletic, “A survey and taxonomy of approaches for mining software repositories in the context of software evolution,” Journal of Software Maintenance and Evolution: Research and Practice, vol.19, no.2, pp.77-131, 2007. 10.1002/smr.344 – reference: [16] M. Brandtner, S.C. Muller, P. Leitner, and H.C. Gall, “Sqa-profiles: Rule-based activity profiles for continuous integration environments,” 2015 IEEE 22nd International Conference on Software Analysis, Evolution and Reengineering (SANER), pp.301-310, IEEE, 2015. 10.1109/saner.2015.7081840 – reference: [12] L. Voinea and A. Telea, “Visual querying and analysis of large software repositories,” Empirical Software Engineering, vol.14, no.3, pp.316-340, 2009. 10.1007/s10664-008-9068-6 – reference: [28] B. Vasilescu, A. Serebrenik, and M. van den Brand, “Comparative study of software metrics aggregation techniques,” Proc. International Worskhop Benevol, vol.2010, 2010. – reference: [1] H. Hemmati, S. Nadi, O. Baysal, O. Kononenko, W. Wang, R. Holmes, and M.W. Godfrey, “The msr cookbook: Mining a decade of research,” 2013 10th IEEE Working Conference on Mining Software Repositories (MSR), pp.343-352, IEEE, 2013. 10.1109/msr.2013.6624048 – reference: [22] M. Rebouças, R.O. Santos, G. Pinto, and F. Castor, “How does contributors' involvement influence the build status of an open-source software project?,” Proc. 14th International Conference on Mining Software Repositories, pp.475-478, IEEE Press, 2017. 10.1109/msr.2017.32 – reference: [34] G. Gousios and D. Spinellis, “Conducting quantitative software engineering studies with alitheia core,” Empirical Software Engineering, vol.19, no.4, pp.885-925, 2014. 10.1007/s10664-013-9242-3 – reference: [20] M.M. Rahman and C.K. Roy, “Impact of continuous integration on code reviews,” Proc. 14th International Conference on Mining Software Repositories, pp.499-502, IEEE Press, 2017. 10.1109/msr.2017.39 – reference: [21] D. Yang, P. Martins, V. Saini, and C. Lopes, “Stack overflow in github: any snippets there?,” Proc. 14th International Conference on Mining Software Repositories, pp.280-290, IEEE Press, 2017. 10.1109/msr.2017.13 – reference: [11] G. Ghezzi, M. Würsch, E. Giger, and H.C. Gall, “An architectural blueprint for a pluggable version control system for software (evolution) analysis,” Proc. Second International Workshop on Developing Tools as Plug-Ins, pp.13-18, IEEE Press, 2012. 10.1109/topi.2012.6229803 – reference: [25] ISO:IEC9126-1, “Software engineering-product quality-part 1: Quality model,” International Organization for Standardization, Geneva, Switzerland, 2001. – reference: [18] J.M. Gonzalez-Barahona, G. Robles, and I. Herraiz, “Challenges in software evolution: the libre software perspective,” International ERCIM-ESF Workshop on Challenges in Software Evolution (ChaSE), 2005. – reference: [19] M. DAmbros, H. Gall, M. Lanza, and M. Pinzger, “Analysing software repositories to understand software evolution,” Software Evolution, pp.37-67, Springer, 2008. – reference: [6] J. Sillito, G.C. Murphy, and K. De Volder, “Questions programmers ask during software evolution tasks,” Proc. 14th ACM SIGSOFT international symposium on Foundations of software engineering, pp.23-34, ACM, 2006. 10.1145/1181775.1181779 – reference: [23] N. Fenton and J. Bieman, Software Metrics: A rigorous and practical approach, CRC Press, 2014. – reference: [30] C. Wohlin, P. Runeson, M. Höst, M.C. Ohlsson, B. Regnell, and A. Wesslén, Experimentation in Software Engineering, Springer Science & Business Media, 2012. – reference: [33] G. Robles, J.M. González-Barahona, C. Cervigón, A. Capiluppi, and D. Izquierdo-Cortázar, “Estimating development effort in free/open source software projects by mining software repositories: a case study of openstack,” Proc. 11th Working Conference on Mining Software Repositories, pp.222-231, ACM, 2014. 10.1145/2597073.2597107 – reference: [13] T. Fritz and G.C. Murphy, “Using information fragments to answer the questions developers ask,” Proc. 32nd ACM/IEEE International Conference on Software Engineering, vol.1, pp.175-184, ACM, 2010. 10.1145/1806799.1806828 – reference: [26] Y. Kanellopoulos, P. Antonellis, D. Antoniou, C. Makris, E. Theodoridis, C. Tjortjis, and N. Tsirakis, “Code quality evaluation methodology using the ISO/IEC 9126 standard,” arXiv preprint arXiv:1007.5117, 2010. – reference: [9] T.D. LaToza, G. Venolia, and R. DeLine, “Maintaining mental models: a study of developer work habits,” Proc. 28th international conference on Software engineering, pp.492-501, ACM, 2006. 10.1145/1134285.1134355 – reference: [3] A.E. Hassan, “The road ahead for mining software repositories,” Frontiers of Software Maintenance, 2008. FoSM 2008., pp.48-57, IEEE, 2008. 10.1109/fosm.2008.4659248 – reference: [10] T.D. LaToza and B.A. Myers, “Hard-to-answer questions about code,” Evaluation and Usability of Programming Languages and Tools, p.8, ACM, 2010. – reference: [7] B. De Alwis and G.C. Murphy, “Answering conceptual queries with ferret,” ACM/IEEE 30th International Conference on Software Engineering, 2008. ICSE'08. pp.21-30, IEEE, 2008. 10.1145/1368088.1368092 – reference: [8] A.J. Ko, R. DeLine, and G. Venolia, “Information needs in collocated software development teams,” Proc. 29th international conference on Software Engineering, pp.344-353, IEEE Computer Society, 2007. 10.1109/icse.2007.45 – reference: [5] V.R. Basili, G. Caldiera, H.D. Rombach, and R. van Solingen, “Goal question metric (GQM) approach,” ed. J. Marciniak, Encyclopedia of Software Engineering, vol.1, pp.578-583, Wiley Online Library, 2002. – reference: [27] H. Washizaki, R. Namiki, T. Fukuoka, Y. Harada, and H. Watanabe,“A framework for measuring and evaluating program source code quality,” Product-Focused Software Process Improvement, pp.284-299, Springer, 2007. – reference: [24] ISO:IEC25010, “Systems and software engineering systems and software quality requirements and evaluation (square) system and software quality models,” International Organisation for Standardisation, Geneva, Switzerland, 2011. – reference: [32] G. Gousios, E. Kalliamvakou, and D. Spinellis, “Measuring developer contribution from software repository data,” Proc. 2008 international working conference on Mining software repositories, pp.129-132, ACM, 2008. 10.1145/1370750.1370781 – reference: [15] M. Brandtner, E. Giger, and H. Gall, “Sqa-mashup: A mashup framework for continuous integration,” Information and Software Technology, vol.65, pp.97-113, 2015. 10.1016/j.infsof.2014.10.004 – reference: [14] Y.-F. Li and H. Zhang, “Integrating software engineering data using semantic web technologies,” Proc. 8th Working Conference on Mining Software Repositories, pp.211-214, ACM, 2011. 10.1145/1985441.1985473 – reference: [2] T. Roehm, R. Tiarks, R. Koschke, and W. Maalej, “How do professional developers comprehend software?,” Proc. 34th International Conference on Software Engineering, pp.255-265, IEEE Press, 2012. 10.1109/icse.2012.6227188 – reference: [4] Y. Sakamoto, S. Matsumoto, S. Saiki, and M. Nakamura, “Visualizing software metrics with service-oriented mining software repository for reviewing personal process,” 2013 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), pp.549-554, IEEE, 2013. 10.1109/snpd.2013.96 – reference: [17] C. Wijesiriwardana, G. Ghezzi, and H. Gall, “A guided mashup framework for rapid software analysis service composition,” 2012 19th Asia-Pacific Software Engineering Conference (APSEC), pp.725-728, IEEE, 2012. 10.1109/apsec.2012.112 – reference: [29] C. Bird, T. Menzies, and T. Zimmerman, The Art and Science of Analyzing Software Data, Elsevier, MA, 2015. – ident: 35 doi: 10.1002/smr.344 – ident: 12 doi: 10.1007/s10664-008-9068-6 – ident: 18 – ident: 17 doi: 10.1109/APSEC.2012.112 – ident: 30 doi: 10.1007/978-3-642-29044-2 – ident: 3 doi: 10.1109/FOSM.2008.4659248 – ident: 11 doi: 10.1109/TOPI.2012.6229803 – ident: 19 doi: 10.1007/978-3-540-76440-3_3 – ident: 21 doi: 10.1109/MSR.2017.13 – ident: 7 doi: 10.1145/1368088.1368092 – ident: 9 doi: 10.1145/1134285.1134355 – ident: 16 doi: 10.1109/SANER.2015.7081840 – ident: 31 – ident: 34 doi: 10.1007/s10664-013-9242-3 – ident: 10 doi: 10.1145/1937117.1937125 – ident: 28 – ident: 4 doi: 10.1109/SNPD.2013.96 – ident: 22 doi: 10.1109/MSR.2017.32 – ident: 26 doi: 10.5121/ijsea.2010.1302 – ident: 24 – ident: 15 doi: 10.1016/j.infsof.2014.10.004 – ident: 8 doi: 10.1109/ICSE.2007.45 – ident: 20 doi: 10.1109/MSR.2017.39 – ident: 5 – ident: 23 doi: 10.1201/b17461 – ident: 27 doi: 10.1007/978-3-540-73460-4_26 – ident: 33 doi: 10.1145/2597073.2597107 – ident: 2 doi: 10.1109/ICSE.2012.6227188 – ident: 6 doi: 10.1145/1181775.1181779 – ident: 29 doi: 10.1016/B978-0-12-411519-4.00001-X – ident: 14 doi: 10.1145/1985441.1985473 – ident: 13 doi: 10.1145/1806799.1806828 – ident: 25 – ident: 1 doi: 10.1109/MSR.2013.6624048 – ident: 32 doi: 10.1145/1370750.1370781 |
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| Title | Fostering Real-Time Software Analysis by Leveraging Heterogeneous and Autonomous Software Repositories |
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