Identifying and Generating Missing Tests using Machine Learning on Execution Traces

Mark Utting; Bruno Legeard; Frederic Dadeau; Frederic Tamagnan; Fabrice Bouquet

doi:10.1109/AITEST49225.2020.00020

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Identifying and Generating Missing Tests using Machine Learning on Execution Traces

Conference paper

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Identifying and Generating Missing Tests using Machine Learning on Execution Traces

Mark Utting, Bruno Legeard, Frederic Dadeau, Frederic Tamagnan and Fabrice Bouquet

2020 IEEE International Conference On Artificial Intelligence Testing (AITest), pp.83-90

IEEE International Conference on Artificial Intelligence Testing, 2nd (Oxford, United Kingdom, 03-Aug-2020–06-Aug-2020)

Institute of Electrical and Electronics Engineers

2020

DOI: https://doi.org/10.1109/AITEST49225.2020.00020

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Abstract

automated regression testing

machine learning

customer traces

clustering

test generation

Testing IT systems has become a major bottleneck for many companies. Besides the growing complexity of such systems, shorter release cycles and increasing quality requirements have led to increased verification and validation costs. However, analysis of existing testing procedures reveals that not all artifacts are exploited to tame this cost increase. In particular, customer traces are usually ignored by validation engineers. In this paper, we use machine learning from execution traces (both customer traces and test execution traces) to identify test needs and to generate new tests in the context of web services and API testing. Log files of customer traces are split into smaller traces (user sessions) then encoded into Pandas DataFrames for data analysis and machine learning. Clustering algorithms are used to analyse the customer traces and compare them with existing system tests, and machine learning models are used to generate missing tests in the desired clusters. The tool-set is implemented in an open-source library called Agilkia.

Details

Title: Identifying and Generating Missing Tests using Machine Learning on Execution Traces
Authors: Mark Utting (Author) - University of the Sunshine Coast, Queensland, USC Business School - Legacy
Bruno Legeard (Author) - Université Bourgogne Franche-Comté
Frederic Dadeau (Author) - Université Bourgogne Franche-Comté
Frederic Tamagnan (Author) - Orange Labs Services
Fabrice Bouquet (Author) - Université Bourgogne Franche-Comté
Publication details: 2020 IEEE International Conference On Artificial Intelligence Testing (AITest), pp.83-90
Conference details: IEEE International Conference on Artificial Intelligence Testing, 2nd (Oxford, United Kingdom, 03-Aug-2020–06-Aug-2020)
Publisher: Institute of Electrical and Electronics Engineers
Date published: 2020
DOI: 10.1109/AITEST49225.2020.00020
Organisation Unit: School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland; USC Business School - Legacy
Language: English
Record Identifier: 99472708602621
Output Type: Conference paper

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