Machine Learning in Failure Regions Detection and Parameters Analysis
Testing automation is one of the challenges facing the software development industry, especially for large complex products. This paper proposes a mechanism called Multi Stage Failure Detector (MSFD) for automating black box testing using different machine learning algorithms. The input to MSFD is t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2019-12-01
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| Series: | International Journal of Networked and Distributed Computing (IJNDC) |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/125928193/view |
| _version_ | 1797711265609547776 |
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| author | Saeed Abdel Wahab Reem El Adawi Ahmed Khater |
| author_facet | Saeed Abdel Wahab Reem El Adawi Ahmed Khater |
| author_sort | Saeed Abdel Wahab |
| collection | DOAJ |
| description | Testing automation is one of the challenges facing the software development industry, especially for large complex products. This paper proposes a mechanism called Multi Stage Failure Detector (MSFD) for automating black box testing using different machine learning algorithms. The input to MSFD is the tool’s set of parameters and their value ranges. The parameter values are randomly sampled to produce a large number of parameter combinations that are fed into the software under test. Using neural networks, the resulting logs from the tool are classified into passing and failing logs and the failing logs are then clustered (using mean-shift clustering) into different failure types. MSFD provides visualization of the failures along with the responsible parameters. Experiments on and results for two real-world complex software products are provided, showing the ability of MSFD to detect all failures and cluster them into the correct failure types, thus reducing the analysis time of failures, improving coverage, and increasing productivity. |
| first_indexed | 2024-03-12T07:04:30Z |
| format | Article |
| id | doaj.art-ca75bd688fe742c29b369804977a7f38 |
| institution | Directory Open Access Journal |
| issn | 2211-7946 |
| language | English |
| last_indexed | 2024-03-12T07:04:30Z |
| publishDate | 2019-12-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Networked and Distributed Computing (IJNDC) |
| spelling | doaj.art-ca75bd688fe742c29b369804977a7f382023-09-02T23:32:51ZengSpringerInternational Journal of Networked and Distributed Computing (IJNDC)2211-79462019-12-018110.2991/ijndc.k.191204.001Machine Learning in Failure Regions Detection and Parameters AnalysisSaeed Abdel WahabReem El AdawiAhmed KhaterTesting automation is one of the challenges facing the software development industry, especially for large complex products. This paper proposes a mechanism called Multi Stage Failure Detector (MSFD) for automating black box testing using different machine learning algorithms. The input to MSFD is the tool’s set of parameters and their value ranges. The parameter values are randomly sampled to produce a large number of parameter combinations that are fed into the software under test. Using neural networks, the resulting logs from the tool are classified into passing and failing logs and the failing logs are then clustered (using mean-shift clustering) into different failure types. MSFD provides visualization of the failures along with the responsible parameters. Experiments on and results for two real-world complex software products are provided, showing the ability of MSFD to detect all failures and cluster them into the correct failure types, thus reducing the analysis time of failures, improving coverage, and increasing productivity.https://www.atlantis-press.com/article/125928193/viewTestingautomationmachine learningclusteringclassification |
| spellingShingle | Saeed Abdel Wahab Reem El Adawi Ahmed Khater Machine Learning in Failure Regions Detection and Parameters Analysis International Journal of Networked and Distributed Computing (IJNDC) Testing automation machine learning clustering classification |
| title | Machine Learning in Failure Regions Detection and Parameters Analysis |
| title_full | Machine Learning in Failure Regions Detection and Parameters Analysis |
| title_fullStr | Machine Learning in Failure Regions Detection and Parameters Analysis |
| title_full_unstemmed | Machine Learning in Failure Regions Detection and Parameters Analysis |
| title_short | Machine Learning in Failure Regions Detection and Parameters Analysis |
| title_sort | machine learning in failure regions detection and parameters analysis |
| topic | Testing automation machine learning clustering classification |
| url | https://www.atlantis-press.com/article/125928193/view |
| work_keys_str_mv | AT saeedabdelwahab machinelearninginfailureregionsdetectionandparametersanalysis AT reemeladawi machinelearninginfailureregionsdetectionandparametersanalysis AT ahmedkhater machinelearninginfailureregionsdetectionandparametersanalysis |