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From past to future: An experience using data mining to guide tests.

Published: 27 January 2023 Publication History

Abstract

It’s common to face errors during the process of software development. Be it an agile or traditional methodology, those errors are documented and registered in tools that allow us to manage and trace them. This data is rich in information about the product we are developing and the processes being used. Therefore, the analysis of this data can give us a better view of the product’s characteristics, its faults and how they affect it’s quality. Having said that, this article relates the use of Machine Learning techniques in a software’s error data base, to identify and classify critical areas in the system, in order to support decision making from the test team, the evolution process and production code maintenance by the developers. Overall, a set of 1045 software defects registries were collected, and we could identify that: (i) 63% of the defects are concentraded in 10 of the 71 existing functionalities, (ii) a functionality has a tendecy to show defects in the last versions of our software, (iii) the software have 4 critical functionalites that concentrate 52% of the reported defects and show recurrent defects.

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cover image ACM Other conferences
SBQS '22: Proceedings of the XXI Brazilian Symposium on Software Quality
November 2022
352 pages
ISBN:9781450399999
DOI:10.1145/3571473
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 ACM 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 January 2023

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Author Tags

  1. Data Mining
  2. Software Incident Repositories
  3. Software Testing

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SBQS '22
SBQS '22: XXI Brazilian Symposium on Software Quality
November 7 - 10, 2022
Curitiba, Brazil

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Overall Acceptance Rate 35 of 99 submissions, 35%

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