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Rapport Année : 2022

Identifying Similar Test Cases That Are Specified in Natural Language

Amey Joshi
  • Fonction : Auteur
  • PersonId : 1209378
Ranit Ganguly
  • Fonction : Auteur
Ritik Gandhi
  • Fonction : Auteur
  • PersonId : 1209379

Résumé

In today's rapidly advancing software industry, we experience exciting technological growth every year in an extensive range of fields such as innovative AI-powered development, cloud and edge computing, machine learning, progressive and lightweight web and mobile applications etc. However, it is not quite relevant to the Software Testing industry. Most companies still rely on the outdated manual testing process despite the availability of Automation testing procedures. It may work for small teams testing the software using a limited number of test cases. Although, with the increase in team size and the number of test cases over time, the cost, effort and time needed to manually validate and review the test cases increase tenfold. It often leads to recurrent and unclear test cases in the test suite, delivered by different employees who often work across teams. These test cases are represented in Natural Language. Also, the redundant test cases can impact the manual testing process by testing the same feature multiple times and can reduce the possibility of writing multiple methods to test the same feature when automating tests in the future. Hence, in this project, we propose to address the problem of similar test cases using an unsupervised learning approach. Additionally, after removing redundancy in the test suite, we intend to identify key features in the software to be tested based on the description of the test cases in the suite, and group multiple test cases into a software feature. This feature can be directly assigned to a Quality Assurance engineer for testing.
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Dates et versions

hal-03911555 , version 1 (23-12-2022)

Identifiants

  • HAL Id : hal-03911555 , version 1

Citer

Amey Joshi, Ranit Ganguly, Ritik Gandhi. Identifying Similar Test Cases That Are Specified in Natural Language. University of Alberta. 2022. ⟨hal-03911555⟩

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