Machine learning applied to software testing: A systematic map** study

VHS Durelli, RS Durelli, SS Borges… - IEEE Transactions …, 2019 - ieeexplore.ieee.org
Software testing involves probing into the behavior of software systems to uncover faults.
Most testing activities are complex and costly, so a practical strategy that has been adopted …

A literature review of using machine learning in software development life cycle stages

S Shafiq, A Mashkoor, C Mayr-Dorn, A Egyed - IEEE Access, 2021 - ieeexplore.ieee.org
The software engineering community is rapidly adopting machine learning for transitioning
modern-day software towards highly intelligent and self-learning systems. However, the …

An empirical study of functional bugs in android apps

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JW Lin, R Jabbarvand, S Malek - 2019 34th IEEE/ACM …, 2019 - ieeexplore.ieee.org
GUI-based testing has been primarily used to examine the functionality and usability of
mobile apps. Despite the numerous GUI-based test input generation techniques proposed in …

Detecting non-crashing functional bugs in Android apps via deep-state differential analysis

J Wang, Y Jiang, T Su, S Li, C Xu, J Lu… - Proceedings of the 30th …, 2022 - dl.acm.org
Non-crashing functional bugs of Android apps can seriously affect user experience. Often
buried in rare program paths, such bugs are difficult to detect but lead to severe …

Software testing effort estimation and related problems: A systematic literature review

I Bluemke, A Malanowska - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
Although testing effort estimation is a very important task in software project management, it
is rarely described in the literature. There are many difficulties in finding any useful methods …