Using Natural Language Processing Techniques to Improve Manual Test Case Descriptions

Viggiato, Markos; Paas, Dale; Buzon, Chris; Bezemer, Cor‐Paul

doi:10.1109/icse-seip55303.2022.9794054

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…A framework was suggested by Viggiato et al [9] for automatically analyzing test cases that are written in natural language and offering useful suggestions for how to improve testing cases. Their framework is made up of reconfigurable elements and modules for analysis that can suggest changes to the terminology of a new test case through language modeling, test steps that might be missing from a new test case through frequent item and association rule mining, and similar test cases that are already present in the test suite through text embedding and clustering.…”

Section: Overview Of the Selected Research Workmentioning

confidence: 99%

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Software Test Case Generation Using Natural Language Processing (NLP): A Systematic Literature Review

Ayenew,

Wagaw

2024

Artificial Intelligence Evolution

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Technologies for natural language processing (NLP) are employed to assist in the analysis and comprehension of human language. Researchers are increasingly focusing on NLP techniques to automate various software development tasks, such as software testing (test case generation). However, choosing the best NLP methods to create automated test cases is never simple. As a result, we look into using NLP techniques to create test cases. We identified 13 research articles published between 2015 and 2023 for this study. As a result, to generate automated test cases, 7 NLP techniques, 2 tools, and 1 framework have been suggested. In addition, 7 NLP algorithms have been discovered in the context of test case generation. Our evaluations indicate that the identified NLP techniques are very useful for automating the generation of test cases. The successful completion of software testing processes (test case generation) therefore requires the use of this approach/technique by software developers, testers, and software engineering teams in general. This paper will be beneficial for researchers engaged in the automation of software testing. Furthermore, it will also be helpful for academic researchers and software engineers (testers) seeking insights into the state of the art in test case generation automation. The paper discusses various tools and methods proposed for test case generation automation, aiding readers in evaluating and selecting the most suitable method for automated test case generation.

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Section: Overview Of the Selected Research Workmentioning

confidence: 99%

“…The authors are Marcos Vijat and others. This article [9] uses NLP technology to stem words and turn sentences into word lists. The group of Goff et al [13] identified distinct sentences and produced a semantic map for each one using natural language processing (Stanford Parser).…”

Section: Overview Of the Selected Research Workmentioning

confidence: 99%

Software Test Case Generation Using Natural Language Processing (NLP): A Systematic Literature Review

Ayenew,

Wagaw

2024

Artificial Intelligence Evolution

View full text Add to dashboard Cite

show abstract

“…We extracted the embeddings of the final convolutional layer of the ResNet-50 model, as is done in prior work [12,40]. To calculate the similarity of the embeddings, we selected cosine similarity, a widely used similarity metric [41,42,43,51].…”

Section: Similarity Metricsmentioning

confidence: 99%