Optimization of the test case minimization algorithm based on forward-propagation in cause-effect graphs

Krupalija, Ehlimana; Cogo, Emir; Bećirović, Šeila; Prazina, Irfan; Pozderac, Damir; Besic, Ingmar

doi:10.1109/infoteh57020.2023.10094154

Cited by 3 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the same work as the previously mentioned forward-propagation paper, we also proposed a test suite minimization algorithm for reducing the number of test cases without removing the most important ones from the test suite. We later optimized this algorithm 36 for achieving even better performances. Other existing test case suite minimization approaches are based on binary trees, 37 decision table merging, 38 and mutation analysis.…”

Section: Cause-effect Graphsmentioning

confidence: 99%

“…The complete test suites for the embedded IoT, web-server and mobile phone subsystems are shown in Tables 2 and 3. Two test suite minimization algorithms were used for reducing the test suite size: basic minimization 20 (which sorts the test cases by the number of active effects and maximizes EC) and optimized minimization 36 (which additionally uses test case strength for further reducing the test suite size without reducing EC). The resulting minimized test suites are marked in blue color (for each test which is contained only in the basic proposed minimization) and green color (for each test which is contained both in the basic and the optimized proposed minimizations).…”

Section: Derivation Of Test Suitesmentioning

confidence: 99%

See 1 more Smart Citation

Usage of cause‐effect graphs for reliability analysis of an embedded IoT alarm software system

Cogo,

Karabegović

et al. 2024

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

Software is a part of most complex systems used for different purposes in today's world. The reliability of these systems must be verified before putting them to use, because their malfunction may lead to catastrophic failure, the loss of human lives, or huge costs. Software reliability can be verified by performing black‐box testing, without knowledge of the complex internal structure of these systems. In this work, an embedded IoT alarm system for detection of intruders and potential robbery is proposed. It is composed of three subsystems: embedded IoT, web‐server, and mobile phone subsystems. Cause‐effect graphing technique was applied for generating three types of black‐box test suites, by using the forward‐propagation and two different minimization algorithms. These test suites were used for black‐box testing the system, which was performed after injecting a maximum of 12 faults into the system in 100 iterations. The resulting software quality and reliability metrics showed that the forward‐propagation algorithm always achieves maximum fault detection rate (FDR), and this algorithm should be used when the highest demand for reliability must be met. The optimized minimization should only be used in cases where testing is very expensive, because it did not find faults in all testing iterations. The basic optimization retained high FDR, while reducing the impact of the minimized test suite and achieved a significant increase of test effect coverage values.

show abstract

Section: Cause-effect Graphsmentioning

confidence: 99%

Section: Derivation Of Test Suitesmentioning

confidence: 99%