Results 21 to 30 of about 308 (122)
The integration of machine learning into automated test generation: A systematic mapping study
Software Testing, Verification and Reliability, Volume 33, Issue 4, June 2023., 2023 Machine learning (ML) may enable effective automated test generation. We characterize research in this area, examining testing practices, researcher goals, ML techniques applied, evaluation, and challenges through a systematic mapping on a sample of 124 publications. The work‐to‐date shows great promise, but there are open challenges regarding training
Afonso Fontes, Gregory Gay
wiley +1 more source
A systematic mapping study for graphical user interface testing on mobile apps
IET Software, Volume 17, Issue 3, Page 249-267, June 2023., 2023 This paper presents a systematic mapping study to gain insight into GUI testing for mobile apps. Through bibliometric analysis, the paper identifies the GUI testing research communities, influential authors, papers and popular topic trends. Additionally, the paper identifies the common approaches and the evaluation metrics used in GUI testing for ...
Liming Nie +4 more
wiley +1 more source
ZaligVinder: A generic test framework for string solvers
Journal of Software: Evolution and Process, Volume 35, Issue 4, April 2023., 2023 The increased interest in string solving in the recent years has made it very hard to identify the right tool to address a particular user's purpose. Generally, it lacks of comparisons between the existing tools on an equal set of benchmarks, especially as a common framework to compare solvers seems to be missing.
Mitja Kulczynski +3 more
wiley +1 more source
Mutation‐inspired symbolic execution for software testing
IET Software, Volume 16, Issue 5, Page 478-492, October 2022., 2022 Abstract Software testing is a complex and costly stage during the software development lifecycle. Nowadays, there is a wide variety of solutions to reduce testing costs and improve test quality. Focussing on test case generation, Dynamic Symbolic Execution (DSE) is used to generate tests with good structural coverage.
Kevin J. Valle‐Gómez +3 more
wiley +1 more source
Malware recognition approach based on self‐similarity and an improved clustering algorithm
IET Software, Volume 16, Issue 5, Page 527-541, October 2022., 2022 Abstract The recognition of malware in network traffic is an important research problem. However, existing solutions addressing this problem rely heavily on the source code and misrecognise vulnerabilities (i.e. incur a high false positive rate (FPR)) in some cases.
Jinfu Chen +5 more
wiley +1 more source
Fostering the diversity of exploratory testing in web applications
Software Testing, Verification and Reliability, Volume 32, Issue 5, August 2022., 2022 Exploratory testing (ET) is an approach to software testing that relies on the expertise of testers. However, testers performing exploratory testing may miss anomalies or unusual interactions proposed by the system. Our approach monitors testers' interactions to suggest, in real time, interactions that increase the diversity of their explorations ...
Julien Leveau +4 more
wiley +1 more source
BUGSJS: a benchmark and taxonomy of JavaScript bugs
Software Testing, Verification and Reliability, Volume 31, Issue 4, June 2021., 2021 In this study, we propose BugsJS, a benchmark of 453 real JavaScript bugs from 10 server‐side programs. We extended BugsJS with a rich web interface for visualizing and dissecting the bugs' information, as well as a programmable API to access the faulty source code and to execute the corresponding test cases, which facilitates conducting highly ...
Péter Gyimesi +6 more
wiley +1 more source
SHFuzz: Selective Hybrid Fuzzing with Branch Scheduling Based on Binary Instrumentation
Applied Sciences, 2020 Hybrid fuzzing is a popular software testing technique that combines random fuzzing with concolic execution. It is widely used in the security domain known for its ability to find deeply hidden vulnerabilities and reach high code coverage. Hybrid fuzzing
Xianya Mi +4 more
doaj +1 more source
StFuzzer: Contribution‐Aware Coverage‐Guided Fuzzing for Smart Devices
Security and Communication Networks, Volume 2021, Issue 1, 2021., 2021 The root cause of the insecurity for smart devices is the potential vulnerabilities in smart devices. There are many approaches to find the potential bugs in smart devices. Fuzzing is the most effective vulnerability finding technique, especially the coverage‐guided fuzzing. The coverage‐guided fuzzing identifies the high‐quality seeds according to the
Jiageng Yang +5 more
wiley +1 more source
Future Internet, 2020 Fuzz testing is a simple automated software testing approach that discovers software vulnerabilities at a high level of performance by using randomly generated seeds.
Fayozbek Rustamov +2 more
doaj +1 more source