Lightweight Assessment of Test-Case Effectiveness Using Source-Code-Quality Indicators

Giovanni Grano

doi:10.1109/TSE.2019.2903057

2021-04-01

Giovanni Grano *

Fabio Palomba

Harald C. Gall

DOI：10.1109/TSE.2019.2903057

原文链接

查看原文

摘要

En 中文

Test cases are crucial to help developers preventing the introduction of software faults. Unfortunately, not all the tests are properly designed or can effectively capture faults in production code. Some measures have been defined to assess test-case effectiveness: the most relevant one is the mutation score, which highlights the quality of a test by generating the so-called mutants, i.e., variations of the production code that make it faulty and that the test is supposed to identify. However, previous studies revealed that mutation analysis is extremely costly and hard to use in practice. The approaches proposed by researchers so far have not been able to provide practical gains in terms of mutation testing efficiency. This leaves the problem of efficiently assessing test-case effectiveness as still open. In this paper, we investigate a novel, orthogonal, and lightweight methodology to assess test-case effectiveness: in particular, we study the feasibility to exploit production and test-code-quality indicators to estimate the mutation score of a test case. We first select a set of 67 factors and study their relation with test-case effectiveness. Then, we devise a mutation score estimation model exploiting such factors and investigate its performance as well as its most relevant features. The key results of the study reveal that our estimation model only based on static features has 86 percent of both F-Measure and AUC-ROC. This means that we can estimate the test-case effectiveness, using source-code-quality indicators, with high accuracy and without executing the tests. As a consequence, we can provide a practical approach that is beyond the typical limitations of current mutation testing techniques.

Keyword:

Testing

Production

Estimation

Measurement

Predictive models

Machine learning

Computational modeling

Automated software testing

mutation testing

software quality