A Slice-Based Change Impact Analysis for Regression Test Case Prioritization of Object-Oriented Programs

TL;DR

This paper introduces a static, slice-based method for prioritizing regression test cases in object-oriented programs by analyzing affected component coupling, leading to earlier fault detection and improved testing efficiency.

Contribution

It proposes a novel static approach using affected slice graphs and ACC values to prioritize test cases based on fault-proneness in object-oriented programs.

Findings

Test cases covering high ACC nodes detect faults earlier.

The approach outperforms some existing techniques in case studies.

Effective in reducing testing time and cost.

Abstract

Test case prioritization focuses on finding a suitable order of execution of the test cases in a test suite to meet some performance goals like detecting faults early. It is likely that some test cases execute the program parts that are more prone to errors and will detect more errors if executed early during the testing process. Finding an optimal order of execution for the selected regression test cases saves time and cost of retesting. This paper presents a static approach to prioritizing the test cases by computing the affected component coupling (ACC) of the affected parts of object-oriented programs. We construct a graph named affected slice graph (ASG) to represent these affected program parts.We determine the fault-proneness of the nodes of ASG by computing their respective ACC values. We assign higher priority to those test cases that cover the nodes with higher ACC values. Our analysis with mutation faults shows that the test cases executing the fault-prone program parts have a higher chance to reveal faults earlier than other test cases in the test suite. The result obtained from seven case studies justifies that our approach is feasible and gives acceptable performance in comparison to some existing techniques.