An Industrial Study of Natural Language Processing Based Test Case Prioritization

Yang, Yilin; Huang, Xinhai; Xue-fei, Hao; Liu, Zicong; Chen, Zhenyu

doi:10.1109/icst.2017.66

Cited by 11 publications

(3 citation statements)

References 3 publications

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“…With approximately 70% of the worldwide effect, China and the United States are best positioned to profit from the upcoming AI boom [6]. According to research [7], different test case prioritization (TCP) strategies have been presented in order to find flaws in the earliest stages. a manual testing method that predicts test case failures that may be applied as a non-code/specification-based heuristic for test choice, prioritizing, and reduction [8].…”

Section: Background Artificial Intelligence Overviewmentioning

confidence: 99%

The Evolving Role of Artificial Intelligence in Software Testing: Prospects and Challenges

2024

IJFMR

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Artificial intelligence (AI) refers to a machine's capacity for operations typically performed by human intelligence, such as learning, thinking, solving problems, and making decisions. Machine learning, neural networks, expert systems, and rule-based systems are all used in artificial intelligence. AI employs methods and algorithms to process data, draw conclusions from patterns and laws, and enhance performance over time. A software application or product's intended functionality is evaluated and verified through the process of software testing. The benefits of testing include the prevention of bugs, decreased development costs, and improved performance. Through test generation, test data generation, and automated test script writing, AI can be used in software testing to enhance the quality of our product and the manual testing processes. Software testing is a time-consuming, laborious, and tiresome process. Automation solutions have been created to help with automating some testing process operations in order to increase quality and delivery time. As continuous integration and delivery (CI/CD) pipelines are added, automation systems gradually lose part of their usefulness. The testing community is looking to AI to fill the gap because AI has the capacity to check the code for flaws and defects without the need for any human intervention and much more quickly than humans. In this study, we want to comprehend the effects of AI technology on various STLC tasks or components of software testing. The study also makes an effort to pinpoint and explain some of the biggest challenges faced by software testers when implementing AI in testing. The report also suggests several significant potential contributions of AI to the field of software testing.

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Section: Background Artificial Intelligence Overviewmentioning

confidence: 99%

The Evolving Role of Artificial Intelligence in Software Testing: Prospects and Challenges

2024

IJFMR

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“…This approach helps to reduce the number of defects and eventually the rework cost. In [8], the authors highlighted the importance of using test case prioritization (TCP) techniques to detect defects in early stages with the aid of NLP assistance. Based in their experiments, the risk strategy achieved the best performance across the other approaches [8].…”

Section: Literature Reviewmentioning

confidence: 99%

How to Generate and Import Functional Test Cases into a Project Management Software System using Natural Language Processing

Reyna¹,

Simske²

2022

archiving

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The main purpose of software testing is to identify what the software does and whether it matches its functional expectations. Applying a test plan allows one to prevent problems in early stages, identifying and addressing solutions before a project goes into production. Test cases play an important role during the software testing phase. A test case is a document with comprehensive details and sequences of actions to guide the software tester through the steps that need to be taken and the outputs that are expected.The proposed system generates test cases based on scraped data that are used to interact with Natural Language Processing (NLP) approaches to generate functional test cases. A project management software (e.g., JIRA) is integrated with the JIRA python library to manage the test cases by the software tester.

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“…Each researcher executes the chosen technique based on specific analysis and considerations. Some of the algorithms used include Greedy and Additional Greedy for search-based TCP [37], [40], Firefly Algorithm [38], [78], Neural Network Classifier [44], Ant Colony Optimization [55], [70], FAST Algorithm [79], Support Vector Machine/SVM [80], Genetic [42], [59], [76], [81], Fuzzy Expert [77], Dynamic Programming [45], Recommender System [58], Clustering Technique [73], [82], [83], Particle Swarm Optimization [61], Natural Language Processing (NLP) [74], and Bat-inspired Algorithm [48].…”

Section: ) What Is the State Of The Art For Tcp Based On Tcp Approaches And Techniques? (Rq1)mentioning

confidence: 99%

A Systematic Literature Review on Regression Test Case Prioritization

Rahmani¹,

Ahmad²,

Jalil³

et al. 2021

IJACSA

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Test case prioritization (TCP) is deemed valid to improve testing efficiency, especially in regression testing, as retest all is costly. The TCP schedule the test case execution order to detect bugs faster. For such benefit, test case prioritization has been intensively studied. This paper reviews the development of TCP for regression testing with 48 papers from 2017 to 2020. In this paper, we present four critical surveys. First is the development of approaches and techniques in regression TCP studies, second is the identification of software under test (SUT) variations used in TCP studies, third is the trend of metrics used to measure the TCP studies effectiveness, and fourth is the stateof-the-art of requirements-based TCP. Furthermore, we discuss development opportunities and potential future directions on regression TCP. Our review provides evidence that TCP has increasing interests. We also discovered that requirement-based utilization would help to prepare test cases earlier to improve TCP effectiveness.

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An Industrial Study of Natural Language Processing Based Test Case Prioritization

Cited by 11 publications

References 3 publications

The Evolving Role of Artificial Intelligence in Software Testing: Prospects and Challenges

The Evolving Role of Artificial Intelligence in Software Testing: Prospects and Challenges

How to Generate and Import Functional Test Cases into a Project Management Software System using Natural Language Processing

A Systematic Literature Review on Regression Test Case Prioritization

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