Software Fault Localisation via Probabilistic Modelling

Johnson, Colin

Abstract

Software development is a complex activity requiring intelligent action. This paper explores the use of an AI technique for one step in software development, viz. detecting the location of a fault in a program. A measure of program progress is proposed, which uses a Naïve Bayes model to measure how useful the information that has been produced by the program to the task that the program is tackling. Then, deviations in that measure are used to find the location of faults in the code. Experiments are carried out to test the effectiveness of this measure.

Citation

Johnson, C. (2020). Software Fault Localisation via Probabilistic Modelling. In Artificial Intelligence XXXVII: 40th SGAI International Conference on Artificial Intelligence, AI 2020, Cambridge, UK, December 15–17, 2020: Proceedings (259-272). https://doi.org/10.1007/978-3-030-63799-6_20

• Presentation Conference Type: Edited Proceedings
• Conference Name: 40th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (AI 2020)
• Start Date: Dec 15, 2020
• End Date: Dec 17, 2020
• Acceptance Date: Sep 13, 2020
• Online Publication Date: Dec 8, 2020
• Publication Date: Dec 8, 2020
• Deposit Date: Feb 22, 2021
• Publicly Available Date: Dec 9, 2021
• Publisher: Springer Publishing Company
• Pages: 259-272
• Series Title: Lecture Notes in Computer Science
• Series Number: 12498
• Series ISSN: 0302-9743
• Book Title: Artificial Intelligence XXXVII: 40th SGAI International Conference on Artificial Intelligence, AI 2020, Cambridge, UK, December 15–17, 2020: Proceedings
• ISBN: 9783030637989
• DOI: https://doi.org/10.1007/978-3-030-63799-6_20
• Public URL: https://nottingham-repository.worktribe.com/output/5345013
• Publisher URL: https://link.springer.com/chapter/10.1007/978-3-030-63799-6_20