Log in

A Novel Framework For Optimal Test Case Generation and Prioritization Using Ent-LSOA And IMTRNN Techniques

  • Published:
Journal of Electronic Testing Aims and scope Submit manuscript

Abstract

Test Case Generation (TCG) generates various types of tests, including functional tests, performance tests, security tests, and reliability tests to ensure software quality, while Test Case Prioritization (TCP) prioritizes the generated tests. However, the previous studies had challenges, including resource constraints, detecting crucial requirements, and automating the Test Case (TC) process efficiently. Additionally, the process is costlier and takes a maximum time duration that affects the effective performance. Therefore, an effective framework is proposed to overcome such issues by optimizing TCG and TCP processes effectively. The proposed work starts with the generation of a Unified Modeling Language (UML) diagram from historical project source code, which is then converted into a Comma-Separated Value (CSV) format. Then, the feature extraction is performed on this CSV file, followed by optimal TCG using the Entropy-based Locust Swarm Optimization Algorithm (Ent-LSOA). Additionally, factors are extracted and reduced from the historical project source code using Pearson Correlation Coefficient-Generalized Discriminant Analysis (PCC-GDA). Finally, the optimal TCs and selected factors are prioritized with the highest accuracy and recall of 96.89% and 96.92%, respectively using an Interpolated Multiple Time scale Recurrent Neural Network (IMTRNN). Thus, the proposed work outperformed the existing techniques by providing an efficient solution for TCG and TCP in software testing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data Availability

Dataset for Test case Generation and prioritization https://zenodo.org/records/268466; https://drive.google.com/drive/folders/1ifx1QOoyouqV99w_un_e8w9du3_Q4-v9?usp=drive_link

References

  1. Ahmed M, Nasser AB, Zamli KZ (2022) Construction of Prioritized T-Way Test Suite Using Bi-Objective Dragonfly Algorithm. IEEE Access 10:71683–71698

    Article  Google Scholar 

  2. Bagherzadeh M, Kahani N, Briand L (2022) Reinforcement Learning for Test Case Prioritization. IEEE Trans Software Eng 48(8):2836–2856

    Article  Google Scholar 

  3. Barisal SK, Chauhan SP, Dutta A, Godboley S, Sahoo B, Mohapatra DP (2022) BOOMPizer: Minimization and prioritization of CONCOLIC based boosted MC/DC test cases. J King Saud Univ - Computer Inf Sci. pp 1–20

    Google Scholar 

  4. Bajaj A, Sangwan OP (2021) Discrete and combinatorial gravitational search algorithms for test case prioritization and minimization. Int J Inf Technol (Singapore) 13(2):817–823

    Article  Google Scholar 

  5. Bajaj A, Sangwan OP (2021) Discrete cuckoo search algorithms for test case prioritization. Appl Soft Comput 11:1–18

    Google Scholar 

  6. Bajaj A, Sangwan OP (2021) Tri-level regression testing using nature-inspired algorithms. Innovations Syst Softw Eng 17(1):1–16

    Article  Google Scholar 

  7. Birchler C, Khatiri S, Derakhshanfar P, Panichella S, Panichella A (2023) Single and multi-objective test cases prioritization for self-driving cars in virtual environments. ACM T Soft Eng Meth 32(2):1–30

    Article  Google Scholar 

  8. Dai X, Gong W, Gu Q (2021) Automated test case generation based on differential evolution with node branch archive. Comput Ind Eng 156:1–13

    Article  Google Scholar 

  9. Dandan H (2020) A research on automated software test case generation based on control flow. Procedings - 2020 International Conference on E-Commerce and Internet Technology. ECIT, pp 204–207

  10. Gokilavani N, Bharathi B (2021) Test case prioritization to examine software for fault detection using PCA extraction and K-means clustering with ranking. Soft Comput 25(7):5163–5172

    Article  Google Scholar 

  11. Han J, Li Z, Guo J, Zhao R (2020) Convergence based Evaluation Strategies for Learning Agent of Hyper-heuristic Framework for Test Case Prioritization. In proceedings of 2020 IEEE 20th Proc. International Conference on Software Quality, Reliability, and Security, QRS 2020:394–405

    Google Scholar 

  12. Jaffari A, Yoo CJ, Lee J (2020) Automatic test data generation using the activity diagram and search-based technique. Applied Sciences (Switzerland) 10(10):9–13

    Google Scholar 

  13. Jahan H, Feng Z, Mahmud SH (2020) Risk-Based Test Case Prioritization by Correlating System Methods and Their Associated Risks. Arab J Sci Eng 45(8):6125–6138

    Article  Google Scholar 

  14. Khari M (2019) Empirical Evaluation of Automated Test Suite Generation and Optimization. Arab J Sci Eng 45(4):2407–2423

    Article  Google Scholar 

  15. Khari M, Sinha A, Verdu E, Crespo RG (2020) Performance analysis of six meta-heuristic algorithms over automated test suite generation for path coverage-based optimization. Soft Comput 24(12):9143–9160

    Article  Google Scholar 

  16. Li Y, Tao J, Wotawa F (2020) Ontology-based test generation for automated and autonomous driving functions. Inf Softw Technol 117:1–43

    Article  Google Scholar 

  17. Minhas NM, Masood S, Petersen K, Nadeem A (2020) A systematic map** of test case generation techniques using UML interaction diagrams. J Softw: Evol Process 32(6):1–21

    Google Scholar 

  18. Panda N, Mohapatra DP (2021) Test scenario prioritization from user requirements for web-based software. Int J Syst Assur Eng Manag 12(3):361–376

    Article  Google Scholar 

  19. Paiva AC, Restivo A, Almeida S (2020) Test case generation based on mutations over user execution traces. Software Qual J 28(3):1173–1186

    Article  Google Scholar 

  20. Rocha M, Simao A, Sousa T (2021) Model-based test case generation from UML sequence diagrams using extended finite state machines. Softw Qual J 29(3):597–627

    Article  Google Scholar 

  21. Sahin O, Akay B, Karaboga D (2021) Archive-based multi-criteria Artificial Bee Colony algorithm for whole test suite generation. JESTECH 24(3):806–817

    Google Scholar 

  22. Sahoo RR, Ray M (2020) PSO based test case generation for critical path using improved combined fitness function. J King Saud Univ - Comput Inf Sci 32(4):479–490

    Google Scholar 

  23. Sankar SS, Chandra VC (2020). An Ant colony optimization algorithm based automated generation of software test cases. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 12145, pp 231–239

  24. Shah SA, Bukhari SS, Humayun M, Jhanjhi NZ, Abbas SF (2019) Test case generation using unified modeling language. In proceedings of 2019 International Conference on Computer and Information Sciences. ICCIS, pp 1–6

    Google Scholar 

  25. Shin KW, Lim DJ (2020) Model-based test case prioritization using an alternating variable method for regression testing of a UML-based model. Applied Sciences (Switzerland) 10(21):1–23

    Google Scholar 

  26. Singhal S, Jatana N, Subahi AF, Gupta C, Khalaf OI, Alotaibi Y (2022) Fault coverage-based test case prioritization and selection using african buffalo optimization. CMC 74(3):6755–6774

    Article  Google Scholar 

  27. Su W, Li Z, Wang Z, Yang D (2020) A meta-heuristic test case prioritization method based on hybrid model. In proceedings of 2020 International Conference on Computer Engineering and Application. ICCEA, pp 430–435

    Google Scholar 

  28. Yaraghi AS, Bagherzadeh M, Kahani N, Briand LC (2023) Scalable and accurate test case prioritization in continuous integration contexts. IEEE Trans Software Eng 49:1615–1639

    Article  Google Scholar 

  29. Zamani S, Hemmati H (2020) A cost-effective approach for hyper-parameter tuning in search-based test case generation. In proceedings of 2020 IEEE International Conference on Software Maintenance and Evolution. ICSME, pp 418–429

    Google Scholar 

  30. Zhou ZQ, Liu C, Chen TY, Tse TH, Susilo W (2021) Beating random test case prioritization. IEEE Trans Reliab 70(2):654–675

    Article  Google Scholar 

Download references

Funding

The authors received no specific funding for this study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. Tamizharasi.

Ethics declarations

Conflicts of Interest

An authors have no conflicts of interest to declare that are relevant to the content of this article.

Additional information

Responsible Editor: B. Arasteh.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 15 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tamizharasi, A., Ezhumalai, P. A Novel Framework For Optimal Test Case Generation and Prioritization Using Ent-LSOA And IMTRNN Techniques. J Electron Test (2024). https://doi.org/10.1007/s10836-024-06121-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10836-024-06121-x

Keywords

Navigation