Fostering the Diversity of Exploratory Testing in Web Applications

Leveau, Julien; Blanc, Xavier; Réveillère, Laurent; Falleri, Jean‐Rémy; Rouvoy, Romain

doi:10.1109/icst46399.2020.00026

Cited by 2 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In previous work [6], we addressed this objective and proposed an approach to assist developers in increasing the diversity of their ET session in the challenging domain of web applications. We introduced a prediction model based on

n

‐gram language models that listens to traces of the testers' interactions to predict the next interactions associated with their probability.…”

Section: Introductionmentioning

confidence: 99%

Fostering the diversity of exploratory testing in web applications

Leveau

Blanc

Réveillère

et al. 2022

Software Testing Verif & Rel

Self Cite

View full text Add to dashboard Cite

Exploratory testing (ET) is a software testing approach that complements automated testing by leveraging business expertise. It has gained momentum over the last decades as it appeals testers to exploit their business knowledge to stress the system under test (SUT). Exploratory tests, unlike automated tests, are defined and executed on-the-fly by testers. Testers who perform exploratory tests may be biased by their past experience and therefore may miss anomalies or unusual interactions proposed by the SUT. This is even more complex in the context of web applications, which typically expose a huge number of interaction paths to their users. As testers of these applications cannot remember all the sequences of interactions they performed, they may fail to deeply explore the application scope.This paper therefore introduces a new approach to assist testers in widely exploring any web application. In particular, our approach monitors the online interactions performed by the testers to suggest in real-time the probabilities of performing next interactions. Looking at these probabilities, we claim that the testers who favour interactions that have a low probability (because they were rarely performed), will increase the diversity of their explorations. Our approach defines a prediction model, based on n-grams, that encodes the history of past interactions and that supports the estimation of the probabilities. Integrated within a web browser extension, it automatically and transparently injects feedback within the application itself. We conduct a controlled experiment and a qualitative study to assess our approach. Results show that it prevents testers to be trapped in already tested loops, and succeeds to assist them in performing deeper explorations of the SUT.

show abstract

n

‐gram language models that listens to traces of the testers' interactions to predict the next interactions associated with their probability.…”

Section: Introductionmentioning

confidence: 99%

Fostering the diversity of exploratory testing in web applications

Leveau

Blanc

Réveillère

et al. 2022

Software Testing Verif & Rel

Self Cite

View full text Add to dashboard Cite

show abstract

LatteArt: A Platform for Recording and Analyzing Exploratory Testing

Kirinuki¹,

Tajima²,

Tanno³

2023

2023 IEEE Conference on Software Testing, Verification and Validation (ICST)

View full text Add to dashboard Cite

In recent years, large language models (LLMs), such as ChatGPT, have been pivotal in advancing various artificial intelligence applications, including natural language processing and software engineering. A promising yet underexplored area is utilizing LLMs in software testing, particularly in black-box testing. This paper explores the test cases devised by ChatGPT in comparison to those created by human participants. In this study, ChatGPT (GPT-4) and four participants each created black-box test cases for three applications based on specifications written by the authors. The goal was to evaluate the real-world applicability of the proposed test cases, identify potential shortcomings, and comprehend how ChatGPT could enhance human testing strategies. ChatGPT can generate test cases that generally match or slightly surpass those created by human participants in terms of test viewpoint coverage. Additionally, our experiments demonstrated that when ChatGPT cooperates with humans, it can cover considerably more test viewpoints than each can achieve alone, suggesting that collaboration between humans and ChatGPT may be more effective than human pairs working together. Nevertheless, we noticed that the test cases generated by ChatGPT have certain issues that require addressing before use.

show abstract

Fostering the Diversity of Exploratory Testing in Web Applications

Cited by 2 publications

References 20 publications

Fostering the diversity of exploratory testing in web applications

Fostering the diversity of exploratory testing in web applications

LatteArt: A Platform for Recording and Analyzing Exploratory Testing

Contact Info

Product

Resources

About