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Durrelli, V.H.S., Durelli, R.S., Borges, S.S., Endo, A.T., Eler, M.M., Dias, D.R.C. and Guimaraes, M.P. (2019) Machine Learning Applied to Software Testing: A Systematic Mapping Study. IEEE Transactions on Realiability, 68, 1189-1212.
https://doi.org/10.1109/TR.2019.2892517

has been cited by the following article:

• TITLE: Review of Software Model-Checking Techniques for Dealing with Error Detection in Program Codes

  AUTHORS: Ednah Olubunmi Aliyu

  KEYWORDS: Software Model Checking, Symbolic Execution, State Explosion, Abstraction, Test Case Generations

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.16 No.6, June 28, 2023

  ABSTRACT: Debugging software code has been a challenge for software developers since the early days of computer programming. A simple need, because the world is run by software. So perhaps the biggest engineering challenge is finding ways to make software more reliable. This review provides an overview of techniques developed over time in the field of software model checking to solve the problem of detecting errors in program code. In addition, the challenges posed by this technology are discussed and ways to mitigate them in future research and applications are proposed. A comprehensive examination of the various model verification methods used to detect program code errors is intended to lay the foundation for future research in this area.