Identification of Performance Regression Causing Code Modifications Using Memetic Algorithm

Identification of Performance Regression Causing Code Modifications Using Memetic Algorithm
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© 2025 by IJETT Journal
Volume-73 Issue-1
Year of Publication : 2025
Author : Brindha Subburaj, Uma Maheswari J
DOI : 10.14445/22315381/IJETT-V73I1P131

How to Cite?
Brindha Subburaj, Uma Maheswari J, "Identification of Performance Regression Causing Code Modifications Using Memetic Algorithm," International Journal of Engineering Trends and Technology, vol. 73, no. 1, pp. 357-370, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I1P131

Abstract
Regression testing in software development is a vital and inevitable process performed to ensure that the modifications made to the code do not affect the overall quality of the software. Conducting performance regression tests each and every time when we do some modifications to the code is costlier. Thus, it would be better if we could identify the code modifications that may lead to performance regression and apply regression tests only during such code modification instances. The multi-objective optimization problem formulated includes detecting the code modification that causes performance regression. In this paper, we propose a memetic algorithm named Memetic algorithm using NSGA-II and Local Search (MNSLS), where NSGA-II algorithm with controlled elitism technique is used for global search along with a new improved and controlled local search method. These global and local search techniques improve the exploration and exploitation properties of the algorithm and help to find fitter solutions. MNSLS is used to optimize the identification rules, which could characterize and identify the code modifications that pose a problem to the software quality by finding solutions with a better trade-off between the hit and dismiss rates as objectives. The performance of the proposed algorithm is evaluated using a set of around 8000 Git project commits. The multi-objective optimization results are compared with other evolutionary algorithms using the Hypervolume metric and Mann-Whitney U test. The proposed method is further compared with another evolutionary-based regression identification method called PRICE. The results of the above analysis show that the proposed MNSLS algorithm-based regression identification method is more efficient than other methods.

Keywords
Performance Regression, Evolutionary Algorithm, Local Search, Memetic Algorithm, MNSLS.

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