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Regression Tests Provenance Data in the Continuous Software Engineering Context

Authors:

Heleno de S. Campos Junior,

Camila Acácio de Paiva,

Marco Antônio P. Araújo,

José Maria N. David,

Fernanda CamposAuthors Info & Claims

SAST '17: Proceedings of the 2nd Brazilian Symposium on Systematic and Automated Software Testing

Article No.: 10, Pages 1 - 6

https://doi.org/10.1145/3128473.3128483

Published: 18 September 2017 Publication History

Abstract

Regression tests are executed after every change in software. In a software development environment that adopts Continuous Software Engineering practices such as Continuous Integration, Continuous Delivery and Continuous Deployment, software is changed, built and tested many times. Every regression test execution may include different situations and problems that are treated in isolated way. Data provenance is concerned with the origins and processes that some data has gone through, until it becomes information. Ontologies are formal models that contain axioms and relationships between classes and individuals from a specific context and can be used to infer implicit knowledge. Considering that Continuous Software Engineering activities are based on feedback cycles, in this paper, we propose an architecture based on the use of an ontology and provenance model to capture and provide regression tests data to support the continuous improvement of software testing processes. Moreover, using ontology and provenance to track execution performance and issues in this scenario, may increase the chances of those issues not happening again, since practitioners can address and solve them for future executions.

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Cited By

Sáenz-Adán CGarcía-Izquierdo FPérez BHuynh TMoreau L(2022)Automated and non-intrusive provenance capture with UML2PROVComputing10.1007/s00607-021-01012-x104:4(767-788)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s00607-021-01012-x
Tran HUnterkalmsteiner MBörstler JAli N(2021)Assessing test artifact quality—A tertiary studyInformation and Software Technology10.1016/j.infsof.2021.106620139:COnline publication date: 23-Aug-2021
https://dl.acm.org/doi/10.1016/j.infsof.2021.106620
Sun ZHu CLi CWu L(2020)Domain Ontology Construction and Evaluation for the Entire Process of Software TestingIEEE Access10.1109/ACCESS.2020.30371888(205374-205385)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3037188
Show More Cited By

Index Terms

Regression Tests Provenance Data in the Continuous Software Engineering Context
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation

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SAST '17: Proceedings of the 2nd Brazilian Symposium on Systematic and Automated Software Testing

September 2017

100 pages

ISBN:9781450353021

DOI:10.1145/3128473

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SBC: Sociedade Brasileira de Computação

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Association for Computing Machinery

New York, NY, United States

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Published: 18 September 2017

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SAST '17

SAST '17: 2nd Brazilian Symposium on Systematic and Automated Software Testing

September 18 - 19, 2017

Fortaleza, Brazil

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SAST '17 Paper Acceptance Rate 11 of 16 submissions, 69%;

Overall Acceptance Rate 45 of 92 submissions, 49%

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Cited By

Sáenz-Adán CGarcía-Izquierdo FPérez BHuynh TMoreau L(2022)Automated and non-intrusive provenance capture with UML2PROVComputing10.1007/s00607-021-01012-x104:4(767-788)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s00607-021-01012-x
Tran HUnterkalmsteiner MBörstler JAli N(2021)Assessing test artifact quality—A tertiary studyInformation and Software Technology10.1016/j.infsof.2021.106620139:COnline publication date: 23-Aug-2021
https://dl.acm.org/doi/10.1016/j.infsof.2021.106620
Sun ZHu CLi CWu L(2020)Domain Ontology Construction and Evaluation for the Entire Process of Software TestingIEEE Access10.1109/ACCESS.2020.30371888(205374-205385)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3037188
Dadkhah MAraban SPaydar S(2020)A systematic literature review on semantic web enabled software testingJournal of Systems and Software10.1016/j.jss.2019.110485162:COnline publication date: 1-Apr-2020
https://dl.acm.org/doi/10.1016/j.jss.2019.110485

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