Regression Test Cases Prioritization Using Clustering and Code Change Relevance

Abstract

Regression testing is important for maintaining software quality. However, the cost of regression testing is relatively high. Test case prioritization is one way to reduce this cost. Test case prioritization techniques sort test cases for regression testing based on their importance. In this paper, we design and implement a test case prioritization method based on the location of a change. The method consists of three steps: (1) clustering test cases, (2) prioritizing the clusters with respect to the relevance of the clusters to a code change, and (3) test case prioritization within each cluster based on metrics. We propose a metric for measuring test case importance based on Requirement Complexity, Code Complexity, and Code Coverage. To evaluate our method, we apply it on a launch interceptor problem program, and measure the inclusiveness and precision for clusters of test cases with respect to code change in specific test cases. Our results show that our proposed change-based prioritization method increases the likelihood of executing more relevant test cases earlier.

Functional Requirement-Based Test Case Prioritization in Regression Testing: A Systematic Literature Review

“…Thus, requirement clustering is added in the existing literature on TCP. We also see that Haratay et al [54] used requirements-based clustering approach to improve the number of faults. Bother of the latter-mentioned approaches achieve a higher number of test case execution.…”

“…[52] An extended metric-based approach that identifies fault-prone parts of a software Journal 11 [53] A TCP approach that uses requirements coverage, faults detection and execution time Conference 12 [54] Proposing a metric-based TCP approach using requirement complexity, code complexity, and code coverage Journal 13 [55] Proposing a TCP approach that uses user stories and clusters of test cases for regression testing of agile projects Journal 14 [56] Proposing a TCP approach that uses dissimilarity historical information between various versions of software Furthermore, Wang and Zeng in a primary study [51], examined the historical data to prioritize the test cases. They found that all test cases could not be executed due to cost and time constraints, therefore, they assigned probability values to test cases, and executed only those test cases with the high probabilities.…”

“…Haraty et al [54] proposed a new change-based TCP method for generating the test cases and rerunning these test cases to ensure that software were bug free. Newly proposed TCP method is based on clustering, which is derived from three important test case factors: code complexity, "Code Coverage" (CC), and "Requirement Complexity" (RC).…”

Test case prioritization and distributed testing of object-oriented program

“…Eleven metrics were used depending on accuracy level obtained. The work in [26] estimates code complexity based on statement count. The number of decision nodes is multiplied by 5 and added to the count of nondecision statements in the program to represent code complexity.…”

Hybrid approach of prioritizing Design based and Risk based test cases by Partition Clustering

“…K.Mumtaz, [19] presented a novel density based k-means clustering algorithm to overcome the drawbacks of DBSCAN. Ramzi A. Haraty [20] designed and implemented a cost effective test case prioritization method based on the location of a change. They proposed a metric for measuring test case importance based on Requirement Complexity, Code Complexity, and Code Coverage.…”