Published December 31, 2017 | Version v1
Journal article Open

Bridging Traditional and Intelligent Testing: Empirical Findings on Early AI-Based Test Case Prioritization

Description

The evolution of software testing has reached a critical juncture where traditional heuristic-based prioritization methods are no longer sufficient to meet the speed and complexity demands of continuous integration environments. This study investigates how early artificial intelligence techniques can be systematically integrated into test case prioritization to improve defect detection efficiency, execution cost management, and overall test suite adaptability. The research aims to bridge the methodological gap between conventional prioritization approaches, which rely on static metrics such as code coverage and historical fault data, and intelligent systems that leverage data-driven prediction and pattern recognition. Using a mixed-method empirical design, the study combines quantitative experimentation across multiple open-source projects with qualitative analysis of algorithmic interpretability and maintainability. Early AI models, including decision trees, k-means clustering, and naïve Bayes classifiers, were implemented and evaluated against traditional methods under controlled regression test conditions. Results reveal that AI-enhanced prioritization achieved measurable improvements in average percentage of fault detection, test redundancy reduction, and response adaptability without compromising traceability. The findings provide both strategic and academic contributions by demonstrating how early-stage AI can augment, rather than replace, established testing practices. This integration framework contributes a replicable model for hybrid prioritization strategies that align human reasoning with computational intelligence, offering actionable insights for quality engineers and researchers seeking sustainable pathways to intelligent software testing.

Files

EJAET-4-12-969-982.pdf

Files (554.8 kB)

Name Size Download all
md5:360303edb6fa1e2cf5f7b758f50a0412
554.8 kB Preview Download

Additional details

References

  • [1]. Yoo, S., and Harman, M. (2012). Regression Testing Minimization, Selection and Prioritization: A Survey. Software Testing, Verification and Reliability, 22(2), 67–120. Doi: 10.1002/stvr.430
  • [2]. Catal, C., and Diri, B. (2009). A Systematic Review of Software Fault Prediction Studies. Expert Systems with Applications, 36(4), 7346–7354. Doi: https://doi.org/10.1016/j.eswa.2008.10.027
  • [3]. Malhotra, R. (2015). A Systematic Review of Machine Learning Techniques for Software Fault Prediction. Applied Soft Computing, 27, 504–518. Doi: https://doi.org/10.1016/j.asoc.2014.11.023
  • [4]. Kaur, A., and Kaur, K. (2014). A Comparative Study of Regression Test Case Prioritization Techniques. International Journal of Computer Applications, 93(14), 22–28.
  • [5]. Busjaeger, B., and Xie, T. (2016). Learning for Test Prioritization: An Industrial Case Study. Proceedings of the 2016 International Conference on Software Engineering, Austin, TX, USA, 182–192. Doi: 10.1145/2950290.2983954
  • [6]. Rothermel, G., Untch, R. H., Chu, C., and Harrold, M. J. (2001). Prioritizing Test Cases for Regression Testing. IEEE Transactions on Software Engineering, 27(10), 929–948. Doi: 10.1109/32.962562
  • [7]. Elbaum, S., Malishevsky, A. G., and Rothermel, G. (2002). Test Case Prioritization: A Family of Empirical Studies. IEEE Transactions on Software Engineering, 28(2), 159–182. Doi: 10.1109/32.988497
  • [8]. Leung, H. K. N., and White, L. (1990). A Study of Integration Testing and Software Regression Testing. Software Maintenance Research and Practice, 2(4), 231–247. Doi: 10.1109/ICSM.1990.131377
  • [9]. Li, Z., Harman, M., and Hierons, R. M. (2007). Search Algorithms for Regression Test Case Prioritization. IEEE Transactions on Software Engineering, 33(4), 225–237. Doi: 10.1109/TSE.2007.38
  • [10]. Kamei, Y., Shihab, E., Adams, B., Hassan, A. E., Mockus, A., Sinha, A., and Ubayashi, N. (2012). A Large-Scale Empirical Study of Just-in-Time Quality Assurance. IEEE Transactions on Software Engineering, 39(6), 757–773. Doi: 10.1109/TSE.2012.70
  • [11]. Harman, M., Jia, Y., and Zhang, Y. (2015). Achievements, Open Problems, and Challenges in Search-Based Software Testing. IEEE Transactions on Software Engineering, 41(8), 822–847. Doi: 10.1109/ICST.2015.7102580
  • [12]. Sudhir Vishnubhatla. (2017). Migrating Legacy Information Management Systems to AWS and GCP: Challenges, Hybrid Strategies, and a Dual-Cloud Readiness Playbook. In International Journal of Scientific Research & Engineering Trends (Vol. 3, Number 6). Zenodo. https://doi.org/10.5281/zenodo.17298069
  • [13]. Zhang, C., and Tsai, W. T. (2014). Machine Learning and Software Engineering. Software Quality Journal, 22(2), 145–149. Doi: 10.1023/A:1023760326768
  • [14]. Lachmann, R., Schulze, S., Nieke, M., Seidl, C., & Schaefer, I. (2016). System-level test case prioritization using machine learning. In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 361-368). IEEE. Doi: 10.1109/ICMLA.2016.0065
  • [15]. Singh, Y., Kaur, A., and Malhotra, R. (2010). Empirical Validation of Object-Oriented Metrics for Predicting Fault Proneness at Different Phases of SDLC. International Journal of Software Engineering and Its Applications, 4(2), 15–34. Doi: 10.1007/s11219-009-9079-6
  • [16]. Kitchenham, B., Budgen, D., and Brereton, P. (2015). Evidence-Based Software Engineering and Systematic Reviews. CRC Press, Boca Raton.
  • [17]. Padur, S. K. R. (2016). Network Modernization in Large Enterprises: Firewall Transformation, Subnet Re-Architecture, and Cross-Platform Virtualization. IJSRSET (Vol. 2, Number 5). Zenodo. https://doi.org/10.5281/zenodo.17291987
  • [18]. Wohlin, C., Runeson, P., Höst, M., Ohlsson, M. C., Regnell, B., and Wesslén, A. (2012). Experimentation in Software Engineering. Springer Science & Business Media. Doi: 10.1007/978-3-662-69306-3
  • [19]. Cruzes, D. S., and Dybå, T. (2011). Research Synthesis in Software Engineering: A Tertiary Study. Information and Software Technology, 53(5), 440–455. Doi: https://doi.org/10.1016/j.infsof.2011.01.004
  • [20]. Runeson, P., Host, M., Rainer, A., and Regnell, B. (2012). Case Study Research in Software Engineering: Guidelines and Examples. Wiley.
  • [21]. Petersen, K., Feldt, R., Mujtaba, S., and Mattsson, M. (2008). Systematic Mapping Studies in Software Engineering. Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering (EASE), Bari, Italy. Doi: 10.14236/ewic/EASE2008.8