Open AccessUsing neural network models in the quality management system for the software defect prediction
Author(s) -
А. Д. Данилов,
D A Samotsvet,
Varvara Mugatina
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/537/4/042038
Subject(s) - computer science , regression testing , artificial neural network , task (project management) , software quality , software , non regression testing , reliability engineering , quality (philosophy) , data mining , software reliability testing , fault (geology) , software performance testing , artificial intelligence , machine learning , software system , software development , software construction , programming language , engineering , philosophy , systems engineering , epistemology , seismology , geology
Reasonable distribution of resources for regression testing execution of software is considered to be the most important task. Finding the best solution for it may significantly reduce expenses on the whole system development. Neural network model may be used for testing management, as it has fault-prediction ability in each program module. Code parameters are independent variables and presence of errors is a dependent value in such model. Neural network can learn on real data – real testing product. Testing results received from different environment may be integrated easily in the knowledge base. This allows neural network to learn during each testing iteration. The module that potentially contains an error is tested at the first place and more thoroughly. Presented method may predict testing results and distribute resources accordingly.