Near Failure Analysis Using Dynamic Behavioural Data

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review


Automated testing is a safeguard against software regression and provides huge benefits. However, it is yet a challenging subject. Among others, there is a risk that the test cases are too specific, thus making them inefficient. There are many forms of undesirable behaviour that are compatible with a typical program’s specification, that however, harm users. An efficient test should provide most possible information in relation to the resources spent. This paper introduces near failure analysis which complements testing activities by analysing dynamic behavioural metrics (e.g., execution time) in addition to explicit output values. The approach employs machine learning (ML) for classifying the behaviour of a program as faulty or healthy based on dynamic data gathered throughout its executions over time. An ML-based model is designed and trained to predict whether or not an arbitrary version of a program is at risk of failure. The very preliminary evaluation demonstrates promising results for feasibility and effectiveness of near failure analysis.

Original languageEnglish
Title of host publicationProduct-Focused Software Process Improvement
Subtitle of host publication23rd International Conference, PROFES 2022, Jyväskylä, Finland, November 21–23, 2022, Proceedings
EditorsDavide Taibi, Marco Kuhrmann, Tommi Mikkonen, Pekka Abrahamsson, Jil Klünder
PublisherSpringer Science and Business Media B.V.
Number of pages8
ISBN (Electronic)978-3-031-21388-5
ISBN (Print)978-3-031-21387-8
Publication statusPublished - 2022 Nov 22
Event23rd International Conference on Product-Focused Software Process Improvement, PROFES 2022 - Jyväskylä, Finland
Duration: 2022 Nov 212022 Nov 23

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd International Conference on Product-Focused Software Process Improvement, PROFES 2022

Subject classification (UKÄ)

  • Software Engineering

Free keywords

  • Dynamic metrics
  • Failure prediction
  • Regression testing


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