Detecting semantic conflicts with unit tests
Artikel i vetenskaplig tidskrift, 2024

While modern merge techniques, such as 3-way and structured merge, can resolve textual conflicts automatically, they fail when the conflict arises not at the syntactic, but at the semantic level. Detecting such semantic conflicts requires understanding the behavior of the software, which is beyond the capabilities of most existing merge tools. Although semantic merge tools have been proposed, they are usually based on heavyweight static analyses, or need explicit specifications of program behavior. In this work, we take a different route and propose SAM (SemAntic Merge), a semantic merge tool based on the automated generation of unit tests that are used as partial specifications of the changes to be merged, and that drive the detection of unwanted behavior changes (conflicts) when merging software. To evaluate SAM's feasibility for detecting conflicts, we perform an empirical study relying on a dataset of more than 80 pairs of changes integrated to common class elements (constructors, methods, and fields) from 51 merge scenarios. We also assess how the four unit test generation tools used by SAM individually contribute to conflict identification. Our results show that SAM performs best when combining only the tests generated by Differential EvoSuite and EvoSuite, and using our proposed testability transformations (nine detected conflicts out of 29). These results reinforce previous findings about the potential of using test-case generation to detect conflicts as a method that is versatile and requires only limited deployment effort in practice.

Semantic conflicts

Differential testing

Behavior change


Léuson Da Silva

École Polytechnique de Montréal

Universidade Federal de Pernambuco

Paulo Borba

Universidade Federal de Pernambuco

Toni Maciel

Universidade Federal de Pernambuco

Wardah Mahmood

Software Engineering 2

Göteborgs universitet

Thorsten Berger

Ruhr-Universität Bochum

Software Engineering 2

Göteborgs universitet

Joao Moisakis

Universidade Federal de Pernambuco

Aldiberg Gomes

Universidade Federal de Pernambuco

Vinícius Leite

Universidade Federal de Pernambuco

Journal of Systems and Software

0164-1212 (ISSN)

Vol. 214 112070


Datavetenskap (datalogi)



Mer information

Senast uppdaterat