OffSide: Learning to Identify Mistakes in Boundary Conditions

Jón Arnar Briem, Jordi Smit, Hendrig Sellik, Pavel Rapoport, Georgios Gousios, Maurício Aniche

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

4 Citations (Scopus)
248 Downloads (Pure)


Mistakes in boundary conditions are the cause of many bugs in software. These mistakes happen when, e.g., developers make use of '<' or '>' in cases where they should have used '<=' or '>='. Mistakes in boundary conditions are often hard to find and manually detecting them might be very time-consuming for developers. While researchers have been proposing techniques to cope with mistakes in the boundaries for a long time, the automated detection of such bugs still remains a challenge. We conjecture that, for a tool to be able to precisely identify mistakes in boundary conditions, it should be able to capture the overall context of the source code under analysis. In this work, we propose a deep learning model that learn mistakes in boundary conditions and, later, is able to identify them in unseen code snippets. We train and test a model on over 1.5 million code snippets, with and without mistakes in different boundary conditions. Our model shows an accuracy from 55% up to 87%. The model is also able to detect 24 out of 41 real-world bugs; however, with a high false positive rate. The existing state-of-the-practice linter tools are not able to detect any of the bugs. We hope this paper can pave the road towards deep learning models that will be able to support developers in detecting mistakes in boundary conditions.
Original languageEnglish
Title of host publicationICSEW'20
Subtitle of host publicationProceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages6
ISBN (Print)978-1-4503-7963-2
Publication statusPublished - 2020
EventICSEW'20: The IEEE/ACM 42nd International Conference on Software Engineering Workshops - Seoul, Korea, Republic of
Duration: 23 May 202029 May 2020


Country/TerritoryKorea, Republic of


  • boundary testing
  • deep learning for software testing
  • machine learning for software engineering
  • machine learning for software testing
  • software engineering
  • software testing


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