Open AccessBayes‐TDG: effective test data generation using Bayesian belief network: toward failure‐detection effectiveness and maximum coverage
Author(s) -
Feyzi Farid,
Parsa Saeed
Publication year - 2018
Publication title -
iet software
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.305
H-Index - 43
eISSN - 1751-8814
pISSN - 1751-8806
DOI - 10.1049/iet-sen.2017.0112
Subject(s) - bayesian network , oracle , computer science , machine learning , path (computing) , probabilistic logic , bayes' theorem , artificial intelligence , inference , test suite , data mining , bayesian probability , test case , regression analysis , software engineering , programming language
This study presents a novel test data generation method called Bayes‐TDG . It is based on principles of Bayesian networks and provides the possibility of making inference from probabilistic data in the model to increase the prime path‐coverage ratio for a given programme under test (PUT). In this regard, a new programme structure‐based probabilistic network, TDG‐NET, is proposed that is capable of modelling the conditional dependencies among the programme basic blocks (BBs) on one hand and conditional dependencies of the transitions between its BBs and input parameters on the other hand. To achieve failure‐detection effectiveness, the authors propose a path selection strategy that works based on the predicted outcome of generated test cases. So, they mitigate the need for a human oracle, and the generated test suite could be directly used in fault localisation. Several experiments are conducted to evaluate the performance of Bayes‐TDG . The results reveal that the method is promising and the generated test suite could be quite effective.