# Software Product Line Maintenance Using Multi-Objective Optimization Techniques
## 1 Introduction  
Software companies have used effective testing techniques such as automated testing to improve the quality of their software products. However, their derived products are selected from a broad product platform, and the testing procedure for a software product line (SPL) can be complicated and expensive. Moreover, the testing process for an SPL is challenging due to the large number of product variants that need to be tested. By producing test cases automatically, this testing procedure can be made more effective and scalable. Software engineering problems can be solved computationally using a metaheuristic strategy, and this is what search-based software engineering (SBSE) represents [1]. Search-based software testing (SBST) assists in generating optimal test cases in terms of time and cost using metaheuristic techniques [2, 3]. Due to the redundancy of test cases, the testing process becomes time-consuming. The various testing optimization methodologieshelp to identify issues related to resource utilization and component coordination. Due to the exponentially growing features in the product line, a classification for product sampling approaches that is based on algorithm type and coverage criteria has been proposed [4]. The process of developing a product line is difficult now because there are thousands of variations [5; 6]. Using interactive tools and procedures that are appropriate for huge data sets and interrelationships, the intriguing efforts can be resolved [7]. The testing of a software product line is a challenging activity, but it will be optimal if all the products in the product line are configured properly [8]. However, it is complex to generate configurations. SPL testing is a hard process because of the complex composition and design of products [9]. Operating systems are excellent examples of highly configurable systems, such as the Linux kernel. Linux may be set up to run on specific hardware, such as an x86 or x64 processor, but there is also the option of configuring the system's functionality. For instance, it is possible to activate support for a built-in webcam video capture tool that is built-in. The configuration options are limited so that the user cannot select settings that will break the system's consistency. In reality, certain features enable millions of configurations. For instance, a video player product line's feature model (_FM_) with 71 features enables it to configure more than 1.65 \(\times\) 10\({}^{13}\) different products. To reduce a product line's test suites, it will be unable to test every product in an SPL at a reasonable cost. The focus of this article was to optimize a large number of test cases in software product lines. For this, five different multi-objective evolutionary algorithms were selected to optimize the SPL test suite. In this context, we addressed the following research questions in this article:  
RQ 1: How will the proposed method, which uses multi-objective optimization (in the case of four objectives optimization), provide the solutions? RQ 2: Which algorithm, employing the four objectives optimization, is the best fit for producing the optimized results? The article is significant due to the following achievements (1) a realistic, genuine application of the methodology involves working with both small and large systems, (2) therefore, both types of systems are validated by the proposed approach along the optimization of different objectives, (3) we can say that the proposed technique can be used for different MOEAs with either complicated or simple systems in our experiments, and (4) it is intended to minimize the number of products and the size of test cases. The rest of the article is organized as follows: Section 2 provides related work on software product line testing and multi-objective algorithms, and Section 3 describes how multi-objective optimization helps to optimize SPL testing. Section 4 presents the method description and testing objectives, and Section 5 describes the experimental setup and results.