FDG: A Precise Measurement of Fault Diagnosability Gain of Test Cases

TL;DR

FDG is a new metric that measures the diagnostic power of individual test cases to improve fault localization, enabling better test suite augmentation with minimal human effort.

Contribution

It introduces FDG, a novel fault diagnosability gain measure that leverages ongoing FL results to prioritize test cases for enhanced fault localization.

Findings

FDG effectively augments test suites for fault localization.

Using FDG improves accuracy metrics significantly with few human judgments.

Evaluation on Defects4J demonstrates its practical utility.

Abstract

The performance of many Fault Localisation (FL) techniques directly depends on the quality of the used test suites. Consequently, it is extremely useful to be able to precisely measure how much diagnostic power each test case can introduce when added to a test suite used for FL. Such a measure can help us not only to prioritise and select test cases to be used for FL, but also to effectively augment test suites that are too weak to be used with FL techniques. We propose FDG, a new measure of Fault Diagnosability Gain for individual test cases. The design of FDG is based on our analysis of existing metrics that are designed to prioritise test cases for better FL. Unlike other metrics, FDG exploits the ongoing FL results to emphasise the parts of the program for which more information is needed. Our evaluation of FDG with Defects4J shows that it can successfully help the augmentation of test suites for better FL. When given only a few failing test cases (2.3 test cases on average), FDG can effectively augment the given test suite by prioritising the test cases generated automatically by EvoSuite: the augmentation can improve the acc@1 and acc@10 of the FL results by 11.6x and 2.2x on average, after requiring only ten human judgements on the correctness of the assertions EvoSuite generates.