Investigating NLP-Based Approaches for Predicting Manual Test Case Failure

Hemmati, Hadi; Sharifi, Fatemeh

doi:10.1109/icst.2018.00038

Cited by 20 publications

(25 citation statements)

References 24 publications

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“…Eventually, we utilize the history-based and TF-IDF methods to estimate the PASS or FAIL with each update's test cases using the LR, NLR, and NN algorithms. [22] As we previously stated and addressed, incorporating AI into software testing would unleash tremendous strength, moving the software testing and development sector in a different direction a period marked by ingenuity and adaptability.…”

Section: Predicting Test Case Failure Using Nlpmentioning

confidence: 97%

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The Impact of NLP on Software Testing

Konreddy¹

2021

JUSST

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The number of applications being built and deployed everyday are increasing by leaps and bounds. To ensure the best user/client experience, the application needs to be free of bugs and other service issues. This marks the importance of testing phase in application development and deployment phase. Basically, testing is dissected into couple of parts being Manual Testing and Automation Testing. Manual testing, which is usually, an individual tester is given software guidance to execute. The tester would post the findings as “passed” or “failed” as per the guidance. But this kind of testing is very costly and time taking process. To eliminate these short comings, automation testing was introduced but it had very little scope and applications are limited. Now, that Artificial Intelligence has been foraying into many domains and has been showing significant impact over those domains. The core principles of Natural Language Processing that can be used in Software Testing are discussed in this paper. It also provides a glimpse at how Natural Language Processing and Software Testing will evolve in the future. Here we focus mainly on test case prioritization, predicting manual test case failure and generation of test cases from requirements utilizing NLP. The research indicates that NLP will improve software testing outcomes, and NLP-based testing will usher in a coming age of software testers work in the not-too-distant times.

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Section: Predicting Test Case Failure Using Nlpmentioning

confidence: 97%

“…This is called Weighted History. [22] Then the coverage of nouns is measured by using a POS tagger and confining only to nouns. Here each noun is used as a unit and the entire test suite is traversed there by calculating TF/IDF for each unit in every test case.…”

Section: Predicting Test Case Failure Using Nlpmentioning

confidence: 99%

The Impact of NLP on Software Testing

Konreddy¹

2021

JUSST

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“…However, all these studies are based on pure manual exploration which highly depends on the expertise and experience of testers. Some studies also optimize manual testing according to task priority [43,44,49], but they also rely on manually labeled data. None of them provides tools to guide testers to test applications more effectively, which is studied in this work.…”

Section: Related Workmentioning

confidence: 99%

“…Within the manual testing, multiple testers mimic users' behaviors to explore different functionalities in the apps aiming to find more bugs [47,58,80,94]. Some researchers optimize manual testing according to test cases and prioritization, but these techniques usually rely on specific data [43,44,49]. To harness crowdsourcing's diversity, crowdsourced testing [45,76,85] recently emerges in software testing which exploits the benefits, effectiveness, and efficiency of crowdsourcing and the cloud platform, to replace conventional manual testing which limits the fixed number of testers.…”

Section: Introductionmentioning

confidence: 99%

“…First, it is time-consuming which requires a large number of testers to manually explore each screen of the app, and testers may execute repeated actions during the exploration [33,34,42]. Second, the performance of manual testing is unstable as it highly depends on the testers' capability and experience, and testers may miss some minor functionalities especially for those unfamiliar apps [42,44,68]. Therefore, the target users of this paper are the testers who are not familiar with the application they are testing.…”

Section: Introductionmentioning

confidence: 99%

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Guided Bug Crush: Assist Manual GUI Testing of Android Apps via Hint Moves

Liu,

Chen,

Wang

et al. 2022

Preprint

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Mobile apps are indispensable for people's daily life. Complementing with automated testing, manual testing is the last line of defence for app quality. However, the repeated actions and easily missing of functionalities make manual testing time-consuming and inefficient. Inspired by the game candy crush with flashy candies as hint moves for players, we propose an approach named NaviDroid for navigating testers via highlighted next operations for more effective and efficient testing. Within NaviDroid, we construct an enriched state transition graph with the triggering actions as the edges for two involved states. Based on it, we utilize the dynamic programming algorithm to plan the exploration path, and augment the GUI with visualized hints for testers to quickly explore untested activities and avoid duplicate explorations. The automated experiments demonstrate the high coverage and efficient path planning of NaviDroid and a user study further confirms its usefulness. It can help us develop more robust software that works in more missioncritical settings, not only by performing more thorough testing with the same effort that has been put in before, but also by integrating these techniques into different parts of development pipeline.

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