GitFL: Automated fault localization for environments where code-changes by multiple developers are tested simultaneously
J.E. van Dorth tot Medler (TU Delft - Mechanical Engineering)
J.C.F. de Winter – Mentor (TU Delft - Human-Robot Interaction)
Kelvin Elsendoorn – Mentor (Adyen N.V)
Y.B. Eisma – Graduation committee member (TU Delft - Human-Robot Interaction)
A.E. Zaidman – Graduation committee member (TU Delft - Software Engineering)
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Abstract
Background: For rigorous software testing, integration and end-to-end tests are essential to ensure the expected behavior of multiple interacting components of the system. When software is subjected to integration or end-to-end tests, it is often unfeasible to test every code change individually, as the runtime of these tests is usually significantly larger compared to unit tests. For this reason, batches of code changes from multiple authors are often tested simultaneously. Problem: An issue with testing multiple changes simultaneously is that it can be unclear which change form which author caused the failure when tests fail, as all changes from all authors included in the test can be at fault. Design: To solve this, a new automatic fault localization algorithm called GitFL is introduced, which combines state-of-the-art fault localization with version control history information for enhanced performance. GitFL was evaluated on a C++ repository at Adyen where tests are considered to be end-to-end. Findings: It showed that the addition of version control history information significantly increases the performance of fault localization for systems where multiple changes are tested simultaneously. Societal implications: This work provides insights on improved fault localization for these systems, which could enable organizations which develop these systems to speed up their testing and development processes. Originality: This work contributes by focusing on fault localization specifically for systems where multiple changes are tested simultaneously, which was not researched before.