Software product line (SPL) engineering paradigm is commonly used to handle commonalities and variabilities of business applications to satisfy the specific needs or goal of a particular market. However, due to time and space complexities, testing all products is not feasible, and SPL testing is proven to be difficult due to a combinatorial explosion of the number of products to be considered. Combinatorial interaction testing (CIT) is suggested to reduce the size of test suites to overcome budget limitations and deadlines. CIT is conducted to fulfill certain quality attributes. This method can be further improvised through the prioritization of list configuration generated from CIT to gain better results in terms of efficiency and scalability, However, to the best of our knowledge, not much research has been done to evaluate existing Test Case Prioritization (TCP) techniques in SPL. This paper provides a survey of existing works on test case prioritization technique. This study provides classification and compares the best technique, trends, gaps and proposed frameworks based on the literature. The evaluation and discussion are using Normative Information Model-based Systems Analysis and Design (NIMSAD) on aspects that include context, content, and validation. The discussion highlights the lack of technique for scalability issue in SPL with most of the work is on academia setting but not on industrial practices.
To achieve the goal of creating products for a specific market segment, implementation of Software Product Line (SPL) is required to fulfill specific needs of customers by managing a set of common features and exploiting the variabilities between the products. Testing product-by-product is not feasible in SPL due to the combinatorial explosion of product number, thus, Test Case Prioritization (TCP) is needed to select a few test cases which could yield high number of faults. Among the most promising TCP techniques is similarity-based TCP technique which consists of similarity distance measure and prioritization algorithm. The goal of this paper is to propose an enhanced string distance and prioritization algorithm which could reorder the test cases resulting to higher rate of fault detection. Comparative study has been done between different string distance measures and prioritization algorithms to select the best techniques for similarity-based test case prioritization. Identified enhancements have been implemented to both techniques for a better adoption of prioritizing SPL test cases. Experiment has been done in order to identify the effectiveness of enhancements done for combination of both techniques. Result shows the effectiveness of the
To achieve the goal of creating products for a specific market segment, implementation of Software Product Line (SPL) is required to fulfill specific needs of customers by managing a set of common features and exploiting the variabilities between the products. Testing product-by-product is not feasible in SPL due to the combinatorial explosion of product number, thus, Test Case Prioritization (TCP) is needed to select a few test cases which could yield high number of faults. Among the most promising TCP techniques is similarity-based TCP technique which consists of similarity distance measure and prioritization algorithm. The goal of this paper is to propose an enhanced string distance and prioritization algorithm which could reorder the test cases resulting to higher rate of fault detection. Comparative study has been done between different string distance measures and prioritization algorithms to select the best techniques for similarity-based test case prioritization. Identified enhancements have been implemented to both techniques for a better adoption of prioritizing SPL test cases. Experiment has been done in order to identify the effectiveness of enhancements done for combination of both techniques. Result shows the effectiveness of the combination where it achieved highest average fault detection rate, attained fastest execution time for highest number of test cases and accomplished 41.25% average rate of fault detection. The result proves that the combination of both techniques improve SPL testing effectiveness compared to other existing techniques.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.