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Automated performance deviation detection across software versions releases

Abderrahmane Benbachir, Isnaldo Francisco De Melo, Michel Dagenais et Bram Adams

Communication écrite (2017)

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Abstract

Performance is an important aspect and critical requirement in multi-process software architecture systems such as Google Chrome. While interacting closely with members of the Google Chrome engineering team, we observed that they face a major challenge in detecting performance deviations between releases, because of their very high release frequency and therefore limited amount of data on each. This paper describes a deep analysis on the data distributions followed by a comparative approach using median based confidence interval for software evaluation. This technique is capable of detecting performance related deviations. It is substantially different from the standard confidence interval, in that it can be used in the presence of outliers and random external influences since the median is less influenced by them. We conducted a bottom-up analysis, using stack traces in a very large pool of releases. The results show that our approach can accurately localize performance deviations at a function-level granularity, using a very small number of trace samples, nearby 5 runs.

Mots clés

Tracing, Performance, Deviation, Quality, Confidence Interval

Sujet(s): 2700 Technologie de l'information > 2700 Technologie de l'information
2700 Technologie de l'information > 2705 Logiciels et développement
2700 Technologie de l'information > 2720 Logiciel de systèmes informatiques
Département: Département de génie informatique et génie logiciel
Organismes subventionnaires: CRSNG/NSERC
Numéro de subvention: CRDPJ468687-14
URL de PolyPublie: https://publications.polymtl.ca/2977/
Nom de la conférence: 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS)
Lieu de la conférence: Prague, Czech Republic
Date(s) de la conférence: 2017-07-25 - 2017-07-29
Maison d'édition: IEEE
DOI: 10.1109/qrs.2017.55
URL officielle: https://doi.org/10.1109/qrs.2017.55
Date du dépôt: 12 févr. 2018 16:35
Dernière modification: 19 avr. 2023 13:06
Citer en APA 7: Benbachir, A., De Melo, I. F., Dagenais, M., & Adams, B. (juillet 2017). Automated performance deviation detection across software versions releases [Communication écrite]. 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS), Prague, Czech Republic (8 pages). https://doi.org/10.1109/qrs.2017.55

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