Test Case Prioritization Using Partial Attention

TL;DR

This paper introduces a novel partial attention mechanism for test case prioritization that improves fault detection rates and significantly reduces computational costs compared to existing methods.

Contribution

It proposes a generic partial attention mechanism integrated with an additional-greedy strategy, leading to a new partition ordering technique for TCP.

Findings

OCP outperforms six state-of-the-art TCP techniques in fault detection.

OCP reduces time costs by 85%-99% compared to existing methods.

Experimental validation on Java and C programs demonstrates effectiveness and efficiency.

Abstract

Test case prioritization (TCP) aims to reorder the regression test suite with a goal of increasing the fault detection rate. Various TCP techniques have been proposed based on different prioritization strategies. Among them, the greedy-based techniques are the most widely-used TCP techniques. However, existing greedy-based techniques usually reorder all candidate test cases in prioritization iterations, resulting in both efficiency and effectiveness problems. In this paper, we propose a generic partial attention mechanism, which adopts the previous priority values (i.e., the number of additionally-covered code units) to avoid considering all candidate test cases. Incorporating the mechanism with the additional-greedy strategy, we implement a novel coverage-based TCP technique based on partition ordering (OCP). OCP first groups the candidate test cases into different partitions and updates the partitions on the descending order. We conduct a comprehensive experiment on 19 versions of Java programs and 30 versions of C programs to compare the effectiveness and efficiency of OCP with six state-of-the-art TCP techniques: total-greedy, additional-greedy, lexicographical-greedy, unify-greedy, art-based, and search-based. The experimental results show that OCP achieves a better fault detection rate than the state-of-the-arts. Moreover, the time costs of OCP are found to achieve 85%-99% improvement than most state-of-the-arts.