Basit öğe kaydını göster

A Method For Selectıng Regressıon Test Cases Based On Software Changes And Software Faults

dc.contributor.advisorTarhan, Ayça
dc.contributor.authorYılmaz, Uğur
dc.date.accessioned2019-04-12T08:36:03Z
dc.date.issued2019
dc.date.submitted2019-01-31
dc.identifier.citation[1] O. Legunsen, F. Hariri, A. Shi, Y. Lu, L. Zhang, and D. Marinov, “An extensive study of static regression test selection in modern software evolution,” Proc. 2016 24th ACM SIGSOFT Int. Symp. Found. Softw. Eng. - FSE 2016, pp. 583–594, 2016. [2] R. Potvin and J. Levenberg, “Why Google stores billions of lines of code in a single repository,” Commun. ACM, vol. 59, no. 7, pp. 78–87, Jun. 2016. [3] S. Biswas, R. Mall, M. Satpathy, and S. Sukumaran, “Regression test selection techniques: A survey,” Inform., vol. 35, no. 3, pp. 289–321, 2011. [4] R. S. Pressman, Software Engineering A Practitioner’s Approach. New York: McGraw Hill, 2002. [5] G. Rothermel and M. J. Harrold, “A framework for evaluating regression test selection techniques,” Proc. 16th Int. Conf. Softw. Eng., pp. 201–210, 1994. [6] R. Kazmi, D. N. A. Jawawi, R. Mohamad, and I. Ghani, “Effective Regression Test Case Selection: A Systematic Literature Review,” ACM Comput. Surv., vol. 50, no. 2, pp. 1–32, 2017. [7] S. Yoo and M. Harman, “Regression Testing Minimisation, Selection and Prioritisation : A Survey,” Test. Verif. Reliab, vol. 00, pp. 1–7, 2007. [8] E. Engström, P. Runeson, and M. Skoglund, “A systematic review on regression test selection techniques,” Inf. Softw. Technol., vol. 52, no. 1, pp. 14–30, Jan. 2010. [9] A. Orso and G. Rothermel, “Software testing: a research travelogue (2000–2014),” in Proceedings of the on Future of Software Engineering - FOSE 2014, 2014, pp. 117–132. [10] A. Shi et al., “Comparing and combining test-suite reduction and regression test selection,” Proc. 2015 10th Jt. Meet. Found. Softw. Eng., pp. 237–247, 2015. [11] R. H. Rosero, O. S. Gómez, and G. Rodríguez, “15 Years of Software Regression Testing Techniques — A Survey,” Int. J. Softw. Eng. Knowl. Eng., vol. 26, no. 05, pp. 675–689, 2016. [12] O. Legunsen, A. Shi, and D. Marinov, “STARTS: STAtic regression test selection,” ASE 2017 - Proc. 32nd IEEE/ACM Int. Conf. Autom. Softw. Eng., no. iv, pp. 949–954, 2017. [13] K. Wang et al., “Towards Refactoring-Aware Regression Test Selection,” p. 12, 2018. [14] M. Gligoric, L. Eloussi, and D. Marinov, “Ekstazi: Lightweight Test Selection,” Proc. - Int. Conf. Softw. Eng., vol. 2, pp. 713–716, 2015. [15] S. Romano, G. Scanniello, G. Antoniol, and A. Marchetto, “SPIRITuS: a SimPle Information Retrieval regressIon Test Selection approach,” Inf. Softw. Technol., vol. 99, pp. 62–80, Jul. 2018. [16] S. Hafez, M. Elnainay, M. Abougabal, and S. Elshehaby, “Potential-fault cache-based regression test selection,” Proc. IEEE/ACS Int. Conf. Comput. Syst. Appl. AICCSA, 2017. [17] L. Zhang, “Hybrid Regression Test Selection,” pp. 199–209, 2018. [18] Y. Pang, X. Xue, and A. S. Namin, “Identifying effective test cases through K-means clustering for enhancing regression testing,” Proc. - 2013 12th Int. Conf. Mach. Learn. Appl. ICMLA 2013, vol. 2, pp. 78–83, 2013. [19] D. Walker and F. Myrick, “Grounded theory: An exploration of process and procedure,” Qual. Health Res., 2006. [20] V. Stray, D. I. K. Sjøberg, and T. Dybå, “The daily stand-up meeting: A grounded theory study,” J. Syst. Softw., vol. 114, pp. 101–124, 2016. [21] IEEE, “IEEE Standard Glossary of Software Terminology.” p. 84, 1990. [22] D. Willmor, S. M. Embury, S. E. -, 2005. ICSM’05. Proceedings of The, and U. 2005, “A safe regression test selection technique for database-driven applications,” in ieeexplore.ieee.org, 2005, vol. 2005, pp. 421–432. [23] A. Nanda, S. Mani, S. Sinha, M. J. Harrold, and A. Orso, “Regression testing in the presence of non-code changes,” Proc. - 4th IEEE Int. Conf. Softw. Testing, Verif. Validation, ICST 2011, pp. 21–30, 2011. [24] H. K. N. Leung, “Insights into Regression Testing,” Proc. Int. Conf. Softw. Maint., pp. 60–69, 1989. [25] T. L. Graves, M. J. Harrold, J.-M. Kim, A. Porter, and G. Rothermel, “An empirical study of regression test selection techniques,” ACM Trans. Softw. Eng. Methodol., vol. 10, no. 2, pp. 184–208, 2001. [26] L. Chen and L. Zhang, “Speeding up Mutation Testing via Regression Test Selection : An Extensive Study,” Proc. - 2018 IEEE 11th Int. Conf. Softw. Testing, Verif. Validation, ICST 2018, pp. 58–69, 2018. [27] H. K. N. Leung and L. White, “A study of integration testing and software regression at the integration level,” Proc. Conf. Softw. Maint. 1990, pp. 290–301, 1990. [28] H. Do and G. Rothermel, “An Empirical Study of Regression Testing Techniques Incorporating Context and Lifetime Factors and Improved Cost-Benefit Models,” Sigsoft’06/Fse-, pp. 141–151, 2006. [29] G. Rothermel and M. J. Harrold, “A safe, efficient regression test selection technique,” ACM Trans. Softw. Eng. Methodol., vol. 6, no. 2, pp. 173–210, 1997. [30] S. Mirarab, S. Akhlaghi, and L. Tahvildari, “Size-constrained regression test case selection using multicriteria optimization,” IEEE Trans. Softw. Eng., vol. 38, no. 4, pp. 936–956, 2012. [31] R. C. Ruth M., “A privacy-aware, end-to-end, CFG-based regression test selection framework for web services using only local information,” 4th Int. Conf. Appl. Digit. Inf. Web Technol. ICADIWT 2011, pp. 13–18, 2011. [32] S. Huang, J. Zhu, and Y. Ni, “ORTS: a tool for optimized regression testing selection,” Proc. 24th ACM SIGPLAN Conf. companion Object oriented Program. Syst. Lang. Appl., pp. 803–804, 2009. [33] W. Jin, A. Orso, and T. Xie, “Automated behavioral regression testing,” ICST 2010 - 3rd Int. Conf. Softw. Testing, Verif. Valid., pp. 137–146, 2010. [34] W. Fu, H. Yu, G. Fan, X. Ji, and X. Pei, “A Regression Test Case Prioritization Algorithm Based on Program Changes and Method Invocation Relationship,” Proc. - Asia-Pacific Softw. Eng. Conf. APSEC, vol. 2017–Decem, pp. 169–178, 2018. [35] P. Dhareula and A. Ganpati, “Prevalent Criteria’s in Regression Test Case Selection Techniques: An Exploratory Study,” 2015. [36] A. Memon and Z. Gao, “Taming Google-Scale Continuous Testing,” in Proceedings of the 39th International Conference on Software Engineering: Software Engineering in Practice Track, 2017. [37] H. Do, “Recent Advances in Regression Testing Techniques,” Adv. Comput., vol. 103, pp. 53–77, Jan. 2016. [38] V. Gupta and D. S. Chauhan, “Hybrid regression testing technique: A multi layered approach,” Proc. - 2011 Annu. IEEE India Conf. Eng. Sustain. Solut. INDICON-2011, 2011. [39] A. S. A. Ansari, K. K. Devadkar, and P. Gharpure, “Optimization of test suite-test case in regression test,” 2013 IEEE Int. Conf. Comput. Intell. Comput. Res. IEEE ICCIC 2013, pp. 3–6, 2013. [40] N. Ye, X. Chen, P. Jiang, W. Ding, and X. Li, “Automatic regression test selection based on activity diagrams,” 2011 5th Int. Conf. Secur. Softw. Integr. Reliab. Improv. - Companion, SSIRI-C 2011, pp. 166–171, 2011. [41] H. Ural and H. Yenigün, “Regression test suite selection using dependence analysis,” J. Softw. Evol. Process, vol. 25, no. 12, pp. 689–709, 2013. [42] C. Tao, B. Li, X. Sun, and C. Zhang, “An Approach to Regression Test Selection Based on Hierarchical Slicing Technique,” in 2010 IEEE 34th Annual Computer Software and Applications Conference Workshops, 2010, pp. 347–352. [43] R. Gupta, M. J. Harrold, and M. L. Soffa, “An approach to regression testing using slicing,” Softw. Maintenance, 1992. Proceerdings., Conf., no. November, pp. 299–308, 1992. [44] Vedpal and N. Chauhan, “Regression test selection for object oriented systems using OPDG and slicing technique,” 2015 2nd Int. Conf. Comput. Sustain. Glob. Dev., pp. 1372–1378, 2015. [45] W. E. Wong, J. R. Horgan, S. London, and H. Agrawal, “A study of effective regression testing in practice,” Proc. Eighth Int. Symp. Softw. Reliab. Eng., pp. 264–274, 1997. [46] D. C. Kung, J. Gao, P. Hsia, J. Lin, and Y. Toyoshima, “Class firewall, test order, and regression testing of COMPONENT IDENTIFICATION METHODS APPLYING METHOD CAL....,” no. October, 1995. [47] F. I. Vokolos and P. G. Frankl, “Pythia: A regression test selection tool based on textual differencing,” Reliab. Qual. Saf. Software-Intensive Syst., pp. 3–21, 1997. [48] V. Garousi, R. Özkan, and A. Betin-Can, “Multi-objective regression test selection in practice: An empirical study in the defense software industry,” Inf. Softw. Technol., no. June, pp. 0–1, 2018. [49] B. Guo, M. Subramaniam, and H. F. Guo, “An approach to regression test selection of adaptive EFSM tests,” Proc. - 5th Int. Conf. Theor. Asp. Softw. Eng. TASE 2011, pp. 217–220, 2011. [50] S. Chen, Z. Chen, Z. Zhao, B. Xu, and Y. Feng, “Using semi-supervised clustering to improve regression test selection techniques,” 2011 Fourth IEEE Int. Conf. Softw. Testing, Verif. Valid., pp. 1–10, 2011. [51] L. Yu, C. Liu, and Y. Zhang, “A multidimensional classification of safe regression test selection techniques,” 2012 Int. Conf. Syst. Informatics, ICSAI 2012, no. Icsai, pp. 2516–2520, 2012. [52] L. C. Briand, Y. Labiche, and S. He, “Automating regression test selection based on UML designs,” Inf. Softw. Technol., vol. 51, no. 1, pp. 16–30, 2009. [53] A. Andrews, S. Elakeili, and A. Alhaddad, “Selective Regression Testing of Safety-Critical Systems: A Black Box Approach,” Proc. - 2015 IEEE Int. Conf. Softw. Qual. Reliab. Secur. QRS-C 2015, pp. 22–31, 2015. [54] E. Fourneret, J. Cantenot, F. Bouquet, B. Legeard, and J. Botella, “SeTGaM: Generalized technique for regression testing based on UML/OCL models,” Proc. - 8th Int. Conf. Softw. Secur. Reliab. SERE 2014, pp. 147–156, 2014. [55] P. Kandil, S. Moussa, and N. Badr, “Cluster-based test cases prioritization and selection technique for agile regression testing,” J. Softw. Evol. Process, vol. 29, no. 6, pp. 1–19, 2017. [56] M. J. Harrold, A. Orso, and M. Lou Soffa, “Using Component Metadata to Support the Regression Testing of Component-Based Software Component Metadata for Regression Test,” Proc. IEEE Int. Conf. Softw. Maint. (ICSM 2001); Florence; Italy; 7 Novemb. 2001 through 9 Novemb. 2001; Code 60554, 2001. [57] M. Skoglund and P. Runeson, “A case study of the class firewall regression test selection technique on a large scale distributed software system,” IEEE/ACM Int. Symp. Empir. Softw. Eng. Meas. - ESEM, vol. 00, no. c, pp. 72–81, 2005. [58] M. J. Harrold et al., “Regression test selection for Java software,” ACM SIGPLAN Not., vol. 36, pp. 312–326, 2001. [59] G. Rothermel, M. J. Harrold, and J. Dedhia, “Regression test selection for C++ software,” Softw. Test. Verif. Reliab., vol. 10, no. 2, pp. 77–109, 2000. [60] G. Xu and A. Rountev, “Regression test selection for AspectJ software,” Proc. - Int. Conf. Softw. Eng., pp. 65–74, 2007. [61] M. Ruth et al., “A Safe Regression Test Selection Technique for Web Services,” in Proceedings of the 31st Annual International Computer Software and Applications Conference - Volume 02, 2007, pp. 0–5. [62] M. Gligoric, L. Eloussi, and D. Marinov, “Practical regression test selection with dynamic file dependencies,” Proc. 2015 Int. Symp. Softw. Test. Anal. - ISSTA 2015, vol. 520, pp. 211–222, 2015. [63] C. Sharma, S. Sabharwal, and R. Sibal, “A Survey on Software Testing Techniques using Genetic Algorithm,” Int. J. Comput. Sci. Issues, vol. 10, no. 1, pp. 381–393, 2013. [64] M. Al-Refai, “Improving model-based regression test selection,” CEUR Workshop Proc., vol. 2019, pp. 507–510, 2017. [65] L. S. De Souza and R. B. C. Prudˆ, “Multi-Objective Test Case Selection : A study of the influence of the Catfish effect on PSO based strategies,” An. do XV Work. Testes e Tolerância a Falhas -WTF 2014, pp. 3–58, 2014. [66] D. Rai and K. Tyagi, “Estimating the Regression Test Case Selection Probability using Fuzzy Rules,” pp. 603–611, 2013. [67] B. Glaser, “Basics of grounded theory analysis: Emergence vs forcing,” 1992. [68] B. Glaser, “Getting out of the data: Grounded theory conceptualization,” 2011. [69] A. Celik, M. Vasic, A. Milicevic, and M. Gligoric, “Regression Test Selection Across JVM Boundaries,” Proc. 2017 11th Jt. Meet. Found. Softw. Eng. - ESEC/FSE 2017, pp. 809–820, 2017. [70] Sujata and G. N. Purohit, “A schema support for selection of test case prioritization techniques,” Int. Conf. Adv. Comput. Commun. Technol. ACCT, vol. 2015–April, pp. 547–551, 2015. [71] A. Orso, N. Shi, and M. J. Harrold, “Scaling regression testing to large software systems,” ACM SIGSOFT Softw. Eng. Notes, vol. 29, no. 6, p. 241, 2004. [72] M. Al-Refai, “MaRTS : A Model-Based Regression Test Selection Approach,” 2017. [73] W. El-hamid and S. El-etriby, “Regression test selection technique for multi-programming language,” INFOS2010 2010 7th Int. Conf. Informatics Syst., 2010. [74] S. Huang, Z. J. Li, J. Zhu, Y. Xiao, and W. Wang, “A novel approach to regression test selection for J2EE applications,” IEEE Int. Conf. Softw. Maintenance, ICSM, no. 61003001, pp. 13–22, 2011. [75] B. Srisura and A. Lawanna, “False test case selection: Improvement of regression testing approach,” 2016 13th Int. Conf. Electr. Eng. Comput. Telecommun. Inf. Technol., pp. 1–6, 2016. [76] M. E. Ruth et al., “Towards Automatic Regression Test Selection for Web Services,” Proc. 31st Annu. Int. Comput. Softw. Appl. Conf. - Vol. 02, vol. 2, p. 1265, 2007. [77] M. Huang, S. Technology, S. Guo, X. Liang, S. Technology, and X. Jiao, “Research on Regression Test Case Selection Based on Improved Genetic Algorithm,” pp. 256–259, 2013. [78] T. Apiwattanapong, A. Orso, and M. J. Harrold, “A differencing algorithm for object-oriented programs,” Proc. - 19th Int. Conf. Autom. Softw. Eng. ASE 2004, pp. 2–13, 2004. [79] A. Panichella, R. Oliveto, M. Di Penta, and A. De Lucia, “Improving multi-objective test case selection by injecting diversity in genetic algorithms,” IEEE Trans. Softw. Eng., vol. 41, no. 4, pp. 358–383, 2015. [80] M. Al-refai, W. Cazzola, and S. Ghosh, “A Fuzzy Logic Based Approach for Model-based Regression Test Selection,” 2017. [81] M. Kim et al., “Efficient regression testing of ontology-driven systems,” Proc. 2012 Int. Symp. Softw. Test. Anal. - ISSTA 2012, p. 320, 2012. [82] R. H. Rosero, O. S. Gomez, and G. Rodriguez, “An Approach for Regression Testing of Database Applications in Incremental Development Settings,” in 2017 6th International Conference on Software Process Improvement (CIMPS), 2017. [83] E. Rogstad, L. Briand, and R. Torkar, “Test case selection for black-box regression testing of database applications,” Inf. Softw. Technol., vol. 55, no. 10, pp. 1781–1795, 2013. [84] M. Khattar, Y. Lamba, and A. Sureka, “SARATHI : Characterization Study on Regression Bugs and Identification of Regression Bug Inducing Changes : A Case-Study on Google Chromium Project Categories and Subject Descriptors,” Proc. 8th India Softw. Eng. Conf. XXX - ISEC ’15, pp. 50–59, 2015. [85] L. Zhang, M. Kim, and S. Khurshid, “FaultTracer: A Change Impact and Regression Fault Analysis Tool for Evolving Java Programs,” Proc. ACM SIGSOFT 20th Int. Symp. Found. Softw. Eng., p. 40:1-40:4, 2012. [86] B. G. Ryder and F. Tip, “Change Impact Analysis for Object-Oriented Programs,” ACM SIGPLAN-SIGSOFT Work. Progr. Anal. Softw. tools Eng., pp. 46–53, 2001. [87] X. Ren, F. Shah, F. Tip, B. G. Ryder, and O. Chesley, “Chianti: A Tool for Change Impact Analysis of Java Programs,” Proceeding OOPSLA ’04 Proc. 19th Annu. ACM SIGPLAN Conf. Object-oriented Program. Syst. Lang. Appl., vol. 39, no. 10, pp. 432–448, 2004. [88] S. Ni and Y. Liu, “A progressive refinement approach for JAVA regression test selection,” 2009 WRI World Congr. Softw. Eng. WCSE 2009, vol. 4, no. 60773105, pp. 170–174, 2009. [89] A. Larprattanakul and T. Suwannasart, “An Approach for Regression Test Case Selection Using Object Dependency Graph,” 2013 5th Int. Conf. Intell. Netw. Collab. Syst., pp. 617–621, 2013. [90] A. B. De Oliveira, S. Fischmeister, A. Diwan, M. Hauswirth, and P. F. Sweeney, “Perphecy: Performance Regression Test Selection Made Simple but Effective,” Proc. - 10th IEEE Int. Conf. Softw. Testing, Verif. Validation, ICST 2017, pp. 103–113, 2017. [91] Z. Chen, H. F. Guo, and M. Song, “Improving regression test efficiency with an awareness of refactoring changes,” Inf. Softw. Technol., vol. 103, no. July, pp. 174–187, 2018. [92] Q. D. Soetens, S. Demeyer, and A. Zaidman, “Change-based test selection in the presence of developer tests,” Proc. Eur. Conf. Softw. Maint. Reengineering, CSMR, pp. 101–110, 2013. [93] H. Aman, T. Nakano, H. Ogasawara, and M. Kawahara, “A Topic Model and Test History-Based Test Case Recommendation Method for Regression Testing,” 2018 IEEE Int. Conf. Softw. Testing, Verif. Valid. Work., pp. 392–397, 2018. [94] M. Al-Refai, S. Ghosh, and W. Cazzola, “Model-Based Regression Test Selection for Validating Runtime Adaptation of Software Systems,” Proc. - 2016 IEEE Int. Conf. Softw. Testing, Verif. Validation, ICST 2016, no. 3, pp. 288–298, 2016. [95] Y. Chen, R. R. L. R. R. L. Probert, and D. P. D. Sims, “Specification-based regression test selection with risk analysis,” Proc. 2002 Conf. …, p. 1, 2002. [96] Q. Farooq, M. Z. Z. Iqbal, Z. I. Malik, and A. Nadeem, “An approach for selective state machine based regression testing,” Proc. 3rd Int. Work. Adv. Model. Test. - A-MOST ’07, no. July, pp. 44–52, 2007. [97] F. Ahmad and Z. H. Qaisar, “Scenario based functional regression testing using Petri net models,” 2013 12th Int. Conf. Mach. Learn. Appl. ICMLA 2013, vol. 2, pp. 572–577, 2013. [98] Q.-U.-A. Farooq, S. Lehnert, and M. Riebisch, “Analyzing model dependencies for rule-based regression test selection,” Model. 2014, vol. P225, pp. 305–320, 2014. [99] R. K. Bhatia, S. Dahiya, and D. Rattan, “Regression test selection using class, sequence and activity diagrams,” IET Softw., vol. 10, no. 3, pp. 72–80, 2016. [100] L. Naslavsky, H. Ziv, and D. J. Richardson, “MbSRT2: Model-based selective regression testing with traceability,” ICST 2010 - 3rd Int. Conf. Softw. Testing, Verif. Valid., pp. 89–98, 2010. [101] Janhavi and A. Singh, “Efficient regression test selection and recommendation approach for component based software,” Proc. 2014 Int. Conf. Adv. Comput. Commun. Informatics, ICACCI 2014, pp. 1547–1553, 2014. [102] Q. Luo, K. Moran, L. Zhang, and D. Poshyvanyk, “How Do Static and Dynamic Test Case Prioritization Techniques Perform on Modern Software Systems? An Extensive Study on GitHub Projects,” IEEE Trans. Softw. Eng., vol. X, no. X, pp. 559–570, 2018. [103] M. Khatibsyarbini, M. A. Isa, D. N. A. Jawawi, and R. Tumeng, “Test case prioritization approaches in regression testing: A systematic literature review,” Inf. Softw. Technol., vol. 93, pp. 74–93, 2018. [104] A. Gambi, J. Bell, and A. Zeller, “Practical Test Dependency Detection,” Proc. - 2018 IEEE 11th Int. Conf. Softw. Testing, Verif. Validation, ICST 2018, pp. 1–11, 2018. [105] “Stack Overflow Developer Survey 2018.” [Online]. Available: https://insights.stackoverflow.com/survey/2018/. [Accessed: 02-Jan-2019]. [106] B. Guo, M. Subramaniam, and P. Chundi, “Analysis of test clusters for regression testing,” Proc. - IEEE 5th Int. Conf. Softw. Testing, Verif. Validation, ICST 2012, p. 736, 2012. [107] C. Science and S. Marcos, “Redroid : A Regression Test Selection Approach for Android Applications,” 2016. [108] V. Channakeshava, S. Lakshmanan, A. Panigrahi, and V. Shanbhag, “ChiARTS - Safe subset-regression test selection for C#,” 4th IASTED Int. Conf. Adv. Comput. Sci. Technol. ACST 2008, no. December, pp. 37–42, 2008. [109] N. Chouhan, M. Dutta, and M. Singh, “A Code Analysis Base Regression Test Selection Technique for D Programming Language,” 2014 Int. Conf. Comput. Intell. Commun. Networks, pp. 1106–1112, 2014. [110] P. R. Srivastava, K. A. V, S. R. V, and R. G, “Regression Testing Techniques for Agent Oriented Software,” 2008 Int. Conf. Inf. Technol., pp. 221–225, 2008. [111] S. Akimoto, R. Yaegashi, and T. Takagi, “Test Case Selection Technique for Regression Testing Using Differential Control Flow Graphs,” pp. 1–3, 2015. [112] Z. Tóth, P. Gyimesi, and R. Ferenc, “A public bug database of GitHub projects and its application in bug prediction,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 9789, pp. 625–638, 2016. [113] S. Elbaum, G. Rothermel, and J. Penix, “Techniques for improving regression testing in continuous integration development environments,” Proc. 22nd ACM SIGSOFT Int. Symp. Found. Softw. Eng. - FSE 2014, pp. 235–245, 2014. [114] G. Salton, A. Wong, C. Y.-C. of the ACM, and undefined 1975, “A vector space model for automatic indexing,” dl.acm.org. [115] Wael H. Gomaa and Aly A. Fahmy, “A Survey of Text Similarity Approaches,” Int. J. Comput. Appl., vol. 68, no. 13, pp. 13–18, 2013. [116] P. Runeson, M. Host, A. Rainer, and B. Regnell, Case Study Research in Software Engineering. 2012. [117] R. K. Yin, Case Study Research. Design and Methods., vol. 5, no. 5. 2009. [118] P. Runeson and M. Höst, “Guidelines for conducting and reporting case study research in software engineering,” Empir. Softw. Eng., vol. 14, no. 2, pp. 131–164, 2009. [119] H. Coles, T. Laurent, C. Henard, M. Papadakis, and A. Ventresque, “PIT: a practical mutation testing tool for Java (demo),” Proc. 25th Int. Symp. Softw. Test. Anal. - ISSTA 2016, pp. 449–452, 2016. [120] J. H. Andrews, L. C. Briand, and Y. Labiche, “Is mutation an appropriate tool for testing experiments?,” Proc. 27th Int. Conf. Softw. Eng. - ICSE ’05, p. 402, 2005. [121] J. Weston, S. Chopra, and A. Bordes, “Memory Networks,” pp. 1–15, 2014. [122] M. Harman, “Making the case for MORTO: Multi objective regression test optimization,” Proc. - 4th IEEE Int. Conf. Softw. Testing, Verif. Valid. Work. ICSTW 2011, no. March 2011, pp. 111–114, 2011. [123] A. Choudhary, A. P. Agrawal, and A. Kaur, “An Effective Approach for Regression Test Case Selection using Pareto based Multi-Objective Harmony Search,” in 1th International Workshop on Search-Based Software Testing, 2018, pp. 1–8. [124] D. Pradhan, S. Wang, S. Ali, T. Yue, and M. Liaaen, “CBGA-ES: A Cluster-Based Genetic Algorithm with Elitist Selection for Supporting Multi-Objective Test Optimization,” Proc. - 10th IEEE Int. Conf. Softw. Testing, Verif. Validation, ICST 2017, pp. 367–378, 2017. [125] Q. Gu, B. Tang, and D. Chen, “Optimal regression testing based on selective coverage of test requirements,” Proc. - Int. Symp. Parallel Distrib. Process. with Appl. ISPA 2010, pp. 419–426, 2010.tr_TR
dc.identifier.urihttp://hdl.handle.net/11655/6555
dc.description.abstractRegression testing is the type of testing in which a modified software is validated to ensure its functionality is not broken. With the increase of modern, agile and large size software systems, regression test selection needs to be efficient, effective and practical to coexist within the software development cycle. To this need, a modern hybrid technique for regression test selection is proposed in this thesis. A detailed literature analysis and a conceptual model are presented in order to better visualize and identify the target concepts of the field. We introduce a technique operating on different granularity levels using difference based techniques of files for both class files and third-party text files. Our technique uses lexical comparison methods for readable files and checksum comparison for any binary files with file or method level granularity. A tunable similarity threshold is offered to users to be used in fulfilling different performance needs. Any available test or fault history data is also used to increase the effectiveness of the proposed technique. We provide an extensive evaluation study in the form of embedded, multiple case study of the proposed technique with other state-of-the-art techniques with respect to performance and cost-efficiency using different open source projects. The results showed that the proposed approach is effective as other state-of-the-art techniques and selects fewer tests while keeping the fault detection rate at a high level.tr_TR
dc.description.tableofcontentsTABLE OF CONTENTS ABSTRACT i ÖZET iii ACKNOWLEDGEMENT v TABLE OF CONTENTS vi LIST OF FIGURES ix LIST OF TABLES x 1. INTRODUCTION 1 2. BACKGROUND 4 2.1. Basic Concepts 4 2.2. Regression Testing Strategies 7 2.3. Regression Test Selection 9 2.3.1. Slicing Approach 11 2.3.2. Data-Flow Approach 11 2.3.3. Firewall Approach 12 2.3.4. Difference Based Approaches 13 2.3.4.1. Code Based Modification Approaches 13 2.3.4.2. Text Based Modification Approaches 14 2.3.5. Cluster Based Approaches 14 2.3.6. Model Based Approaches 14 2.3.7. Graph Walk Approaches 17 2.3.8. Learning Based Approaches 19 2.3.9. Fault Based Approaches 21 2.3.10. Hybrid Approaches 21 3. CONCEPTUAL MODEL 22 3.1. Research Method 22 3.2. Conceptual Model 25 3.3. State-of-the-Art Summary 30 4. PROPOSED TECHNIQUE 34 4.1. The Goal of the Thesis 34 4.2. Proposed Technique 35 5. CASE STUDY 42 5.1. Objective 43 5.2. Design 43 5.3. Results 47 5.3.1. RQ1: How does the proposed approach compare with state-of-the-art approaches in terms of performance with respect to time, selected tests and fault detection capabilities? 47 5.3.1.1. Time 47 5.3.1.2. Selected Test Ratio 52 5.3.1.3. Fault Detection Rate 53 5.3.1.4. Detailed Time Analysis 56 5.3.2. RQ2: How does the proposed approach compare with state-of-the-art approaches in terms of cost-efficiency? 58 5.4. Threats to Validity 59 5.4.1. Construct Validity 59 5.4.2. Internal Validity 59 5.4.3. External Validity 60 5.4.4. Reliability 60 6. CONCLUSION 62 REFERENCES 64 APPENDICES 75 Appendix 1 - Intermediate Conceptual Model Figures 75 Appendix 2 - Papers driven from thesis 79tr_TR
dc.language.isoentr_TR
dc.publisherFen Bilimleri Enstitüsütr_TR
dc.rightsinfo:eu-repo/semantics/openAccesstr_TR
dc.subjectRegression test selection
dc.subjectRegression testing
dc.subjectDynamic analysis
dc.subjectText difference based regression testing
dc.subjectConceptual model
dc.subjectRegresyon test seçimi
dc.subjectRegresyon testi
dc.subjectDinamik analiz
dc.subjectMetin farkı tabanlı regresyon testi
dc.subjectKonsept model
dc.titleA Method For Selectıng Regressıon Test Cases Based On Software Changes And Software Faultstr_eng
dc.title.alternativeRegresyon Testlerinin Seçimi İçin Yazılım Değişikliklerine ve Yazılım Hatalarına Dayalı Bir Yöntemtr_TR
dc.typeinfo:eu-repo/semantics/masterThesistr_TR
dc.description.ozetRegresyon testi, değiştirilmiş bir yazılımda tüm parçaların işlevlerinin doğru çalıştığını güvence etmek için gerçekleştirilen bir test türüdür. Artan modern, çevik ve büyük kapsamlı yazılım sistemleri ile birlikte yazılım geliştirme döngüsünün bir parçası olabilmek için regresyon test seçiminin de etkili, hızlı, verimli ve pratik olması gerekmektedir. Bu amaç doğrultusunda bu tez kapsamında modern hibrit bir regresyon test seçim yöntemi önerilmiştir. Regresyon test alanının etkin kavramlarını tanımlamak ve görsel olarak daha iyi anlamak için ayrıntılı bir literatür taraması ile birlikte bir konsept modeli sunulmuştur. Hem sınıf hem de üçüncü-parti metin dosyaları için fark tabanlı teknikler kullanarak farklı detay katmanlarında çalışabilen bir teknik anlatılmıştır. Önerilen teknik okunabilir dosyalar için sözcük tabanlı karşılaştırma metotları kullanırken, herhangi bir ikili dosyalar için de sağlama toplamı yöntemlerini dosya veya metot detay seviyelerinde kullanmaktadır. Kullanıcıların farklı performans isterlerini karşılamak amacıyla ayarlanabilir bir benzerlik eşiği sunulmuştur. Ayrıca, önerilen tekniğin verimini artırmak için varsa test ve hata verileri de kullanılmıştır. Önerilen teknik ve diğer modern, gelişmiş teknikler, açık kaynak kodlu projeler kullanılarak gömülü, çoklu bir durum çalışması şeklinde geniş çaplı bir değerlendirmeye tabii tutulmuştur. Sonuçlar önerilen tekniğin, diğer teknikler kadar etkili olduğunu ve hata tespit oranını yüksek seviyede tutarken daha az test seçtiğini göstermiştir.tr_TR
dc.contributor.departmentBilgisayar Mühendisliğitr_TR
dc.embargo.termsAcik erisimtr_TR
dc.embargo.lift-


Bu öğenin dosyaları:

Ad:
10236798.pdf
Boyut:
3.055Mb
Biçim:
PDF
Göster/Aç

Bu öğe aşağıdaki koleksiyon(lar)da görünmektedir.

Basit öğe kaydını göster