پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 610 |
| تعداد مقالات | 9,027 |
| تعداد مشاهده مقاله | 67,082,808 |
| تعداد دریافت فایل اصل مقاله | 7,656,333 |
ارائه راهکاری مبتنی بر الگوریتم یادگیری معلم و دانش آموز به منظور کاهش موارد آزمون رگرسیون | ||
| مدل سازی در مهندسی | ||
| دوره 22، شماره 78، آبان 1403، صفحه 1-15 اصل مقاله (1.47 M) | ||
| نوع مقاله: مقاله کامپیوتر | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2024.31194.2490 | ||
| نویسندگان | ||
| محمود دی پیر* 1؛ امیرحسین محمدپور2 | ||
| 1دانشیار، دانشکده رایانه، دانشگاه علوم و فنون هوایی شهید ستاری، تهران، ایران | ||
| 2کارشناسی ارشد، گروه مهندسی کامپیوتر، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران | ||
| تاریخ دریافت: 24 تیر 1402، تاریخ بازنگری: 01 آذر 1402، تاریخ پذیرش: 10 بهمن 1402 | ||
| چکیده | ||
| هدف انتخاب موارد آزمون این است که بتوان زیر مجموعهای انتخاب شود که قابلیت بالقوه شناسایی خطاهای ناشی از تغییرات را داشته باشد. به عبارتی هدف روشهای انتخاب موارد آزمون، کاهش تعداد موارد آزمون بعد از تغییر کد است و بر روی شناسایی بخشهای اصلاح شده برنامه تمرکز دارد. روشهای هوشمند مانند رگرسیون، دقت آزمون را در پروژه‌های نرم افزاری بهبود می‌بخشند و استفاده از الگوریتمهای بهینه سازی در یافتن مقدار بهینه موارد آزمون میتواند از نظر زمان و سرعت هم مفید واقع شود. در این مقاله تکنیکی برای کاهش موارد آزمون رگرسیون مبتنی برروش بهینه سازی معلم- دانش آموز ارائه میشود. این روش از دو مرحله معلم( فاز آموزش) و دانش آموز ( فاز یادگیری) روی مجموعه آزمون تشکیل شده و بر اساس پارامترهای مختلف پیاده‌سازی گردیده است. نتایج آزمایشها نشان داد که استفاده از الگوریتم معلم- دانش آموز، زمان لازم برای کاهش موارد آزمون رگرسیون را تا حدی بهبود میبخشد، هر چند که جواب قطعی را به ما نمی‌دهد و جوابی نزدیک به بهینه را خواهد داد. نتایج حاصل از اجرای رویکرد پیشنهادی با روشهای قبلی انتخاب موارد آزمون مقایسه شده و مشاهده شد که میانگین زمان اجرای موارد آزمون انتخابی، توسط آن بهتر است. | ||
| کلیدواژه‌ها | ||
| آزمون نرم افزار؛ بهینه سازی؛ آزمون رگرسیون؛ الگوریتم یادگیری معلم- دانش آموز | ||
| عنوان مقاله [English] | ||
| Provide a Solution Based on Teacher and Student Learning Algorithm to Reduce Regression Test Cases | ||
| نویسندگان [English] | ||
| Mahmood Deypir1؛ Amirhossein Mohammadpour2 | ||
| 1Associate Professor, Faculty of Computer Engineering, Shahid Sattari Aeronautical University of Science and Technology, Tehran, Iran | ||
| 2MSc, Department of Computer Engineering, South Tehran Branch, Faculty of Technical and Engineering, Islamic Azad University, Tehran, Iran | ||
| چکیده [English] | ||
| The aim of selecting test items is to choose a subset that has the potential to detect errors due to changes within the software. In other words, the purposes of test selection methods is to reduce the number of test cases after changing the code and focus on identifying the modified parts of the program. Intelligent methods such as regression improve the accuracy of tests in software projects, and the use of optimization algorithms to find the optimal amount of test cases can be useful in terms of time and speed, and according to research by examining and optimizing this algorithm in the system. In this paper, a technique for reducing regression test cases based on teacher-student optimization method was presented. This method was studied in two stages of teacher (education phase) and student (learning phase) on the test set and was implemented with different parameters. The experimental results showed that the use of the teacher-student algorithm reduces the time required for the reduction parameters of the regression test to some extent, although it does not give us a definite answer and will give a near-optimal answer. Also, the results of teacher-student algorithm were compared with previous approaches of regression test case reduction. Experimental results show better average execution time for test case selection. . | ||
| کلیدواژه‌ها [English] | ||
| Software test, Optimization, Regression test, Teacher and student Learning algorithm | ||
| مراجع | ||
|
[1] G. Rothermel, R.J. Untch and C. Chu. “Prioritizing Test Cases for Regression Testing.” IEEE Transactions on Software Engineering 27. no.10 (2001): 929-948. [2] M.J. Harrold, J.V. Ronne and C. Hong. “Empirical Studies of TestSuite Reduction.” Software Testing, Verification and Reliability 12. no.4 (2002): 219-249. [3] V.Chaurasia, Y.Chauhan, and K. Thirunavukkarasu. “A survey on test case reduction techniques.” International Journal of Science and Research (IJSR), 2014. [4] Q. Wang, S. Jiang and Y. Zhang. “An approach to generate basis path for programs with exception-handling constructs.” In IACSIT Press, International Conference on Computer Science and Information Technology (ICCSIT) ,2011. [5] R.P. Mahapatra, M. Mohan and A. Kulothungan. “Effective tool for test case Execution time reduction.” In IACSIT, International Symposium on Computing, 2011. [6] S. Dahiya, R.K Bhatia, and D. Rattan. “Regression test selection using class, sequence and activity diagrams.” IET Software 10. no.3 (2016): 72-80. [7] F. Haftman, D. Kossmann, and E. Lo. “A framework for efficient regression tests on database applications.” The VLDB Journal 16 (2007): 145-164. [8] M. Al-Refai, “Improving Model-Based Regression Test Selection. ” in Proceedings of the ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems (MoDELS’18). Austin, TX, USA, 2018. [9] E. Engström, P. Runeson, and M. Skoglund. “A systematic review on regression test selection techniques.” Information and Software Technology 52. no.1 (2010): 14-30. [10] P. Kandil, S. Moussa, and N. Badr. “Cluster-based Test Cases Prioritization and Selection Technique for Agile Regression Testing.” Journal of Software: Evolution and Process 29. no. 6 (2017): e1794. [11] X.Y. Ma, B.K. Sheng, and C.Q.Ye. “Test-suite reduction using genetic algorithm.” Advanced Parallel Processing Technologies: 6th International Workshop, APPT 2005, Hong Kong, China, October 27-28, 2005. Proceedings 6. Springer Berlin Heidelberg, 2005. [12] S. Nayak, C. Kumar, S. Tripathi, N. Mohanty, and V. Baral. “Regression test optimization and prioritization using Honey Bee optimization algorithm with fuzzy rule base.” Soft Computing 25 (2021): 9925-9942. [13] M. Tyagi and S. Malhotra. "Test case prioritization using multi objective particle swarm optimizer." International Conference on Signal Propagation and Computer Technology (ICSPCT 2014). IEEE, 2014. [14] B. Suri and S. Singhal. “Test case selection & prioritization using ant colony optimization.” International Conference on Advanced Computing, Communication and Networks, Chandigarh. vol. 194. 2011. [15] B.A.K.R., ba-quttayyan, H. Mohd, and Y. Yusof. “a critical analysis of swarm intelligence for regression test case prioritization.” Journal of Theoretical and Applied Information Technology 100. no.12 (2022): 3997-4025. [16] M. Khatibsyarbini, M. A. Isa, D.N. Jawawi, H.N.A. Hamed, and M.D.M. Suffian. “Test case prioritization using firefly algorithm for software testing.” IEEE Access 7 (2019): 132360-132373. [17] Vedpal, H. Tanwar, N. Chauhan, and M. Khanna. “Test case prioritization using a Hybrid Chaotic Flower-fruit fly optimization algorithm with multiple objectives.” Multimedia Tools and Applications 83. no. 10 (2024): 28395-28418. [18] A. Bajaj, A. Abraham, S. Ratnoo, and L.A. Gabralla. “Test case prioritization, selection, and reduction using improved quantum-behaved particle swarm optimization.” Sensors 22. no.12 (2022): 4374. [19] E. Engström, P. Runeson, and M. Skoglund. “A systematic review on regression test selection techniques.” Information and Software Technology 52. no.1 (2010): 14-30. [20] O. Dahiya, K. Solanki. “A systematic literature study of regression test case prioritization approaches.” International Journal of Engineering & Technology 7. no. 4 (2018): 2184-2191. [21] M. Khatibsyarbini, M.A. Isa, D.N. Jawawi, and R. Tumeng. “Test case prioritization approaches in regression testing: A systematic literature review.” Information and Software Technology 93 (2018): 74-93. [22] M. Hasnain, I. Ghani, M.F. Pasha, and S.R. Jeong. “Ontology-based regression testing: a systematic literature review.” Applied Sciences 11. no. 20 (2021): 9709. [23] R.V. Rao, V.J. Savsani, and D.P. Vakharia. “Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems.” Computer-aided design 43. no. 3 (2011): 303-315. [24] F. Zou, D. Chen, and Q. Xu. “A survey of teaching–learning-based optimization.” Neurocomputing 335 (2019): 366-383. [25] A. Ebrahimi, A. Hajipour, and H. Tavakoli. "Localization in IoT by using Fractional Order Chaotic Particle Swarm Algorithm Optimization." Journal of Modeling in Engineering 18. no. 60 (2020): 157-168. (in Persian) [26] H. Bigdeli, S.M.S. Mirdamadi, and J. Tayyebi. "A meta-heuristic method for maximum capacity path interdiction problem with multiple attackers." Journal of Modeling in Engineering 20. no. 70 (2022): 133-146. (in Persian) [27] E. Shadkam, and M. Ghayoor. "A new hybrid method DSM for parameter setting of meta-heuristic algorithms." Journal of Modeling in Engineering 19.65 (2021): 161-180. (in Persian) | ||
|
آمار تعداد مشاهده مقاله: 276 تعداد دریافت فایل اصل مقاله: 204 |
||