Applying Software Metrics to RNN for Early Reliability Evaluation
Structural modeling is an important branch of software reliability modeling. It works in the early reliability engineering to optimize the architecture design and guide the later testing. Compared with traditional models using test data, structural models are often difficult to be applied due to lac...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/8814394 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850223733838446592 |
|---|---|
| author | Hao Zhang Jie Zhang Ke Shi Hui Wang |
| author_facet | Hao Zhang Jie Zhang Ke Shi Hui Wang |
| author_sort | Hao Zhang |
| collection | DOAJ |
| description | Structural modeling is an important branch of software reliability modeling. It works in the early reliability engineering to optimize the architecture design and guide the later testing. Compared with traditional models using test data, structural models are often difficult to be applied due to lack of actual data. A software metrics-based method is presented here for empirical studies. The recurrent neural network (RNN) is used to process the metric data to identify defeat-prone code blocks, and a specified aggregation scheme is used to calculate the module reliability. Based on this, a framework is proposed to evaluate overall reliability for actual projects, in which algebraic tools are introduced to build the structural reliability model automatically and accurately. Studies in two open-source projects show that early evaluation results based on this framework are effective and the related methods have good applicability. |
| format | Article |
| id | doaj-art-43f341a8e1924eb0b879fa5bddb4b926 |
| institution | OA Journals |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-43f341a8e1924eb0b879fa5bddb4b9262025-08-20T02:05:50ZengWileyJournal of Control Science and Engineering1687-52491687-52572020-01-01202010.1155/2020/88143948814394Applying Software Metrics to RNN for Early Reliability EvaluationHao Zhang0Jie Zhang1Ke Shi2Hui Wang3School of Medicine Information, Wannan Medical College, Wuhu 241003, ChinaSchool of Computer and Information, Anhui Normal University, Wuhu 241003, ChinaSchool of Computer Science and Technology, Hefei Normal University, Hefei 230601, ChinaSchool of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, ChinaStructural modeling is an important branch of software reliability modeling. It works in the early reliability engineering to optimize the architecture design and guide the later testing. Compared with traditional models using test data, structural models are often difficult to be applied due to lack of actual data. A software metrics-based method is presented here for empirical studies. The recurrent neural network (RNN) is used to process the metric data to identify defeat-prone code blocks, and a specified aggregation scheme is used to calculate the module reliability. Based on this, a framework is proposed to evaluate overall reliability for actual projects, in which algebraic tools are introduced to build the structural reliability model automatically and accurately. Studies in two open-source projects show that early evaluation results based on this framework are effective and the related methods have good applicability.http://dx.doi.org/10.1155/2020/8814394 |
| spellingShingle | Hao Zhang Jie Zhang Ke Shi Hui Wang Applying Software Metrics to RNN for Early Reliability Evaluation Journal of Control Science and Engineering |
| title | Applying Software Metrics to RNN for Early Reliability Evaluation |
| title_full | Applying Software Metrics to RNN for Early Reliability Evaluation |
| title_fullStr | Applying Software Metrics to RNN for Early Reliability Evaluation |
| title_full_unstemmed | Applying Software Metrics to RNN for Early Reliability Evaluation |
| title_short | Applying Software Metrics to RNN for Early Reliability Evaluation |
| title_sort | applying software metrics to rnn for early reliability evaluation |
| url | http://dx.doi.org/10.1155/2020/8814394 |
| work_keys_str_mv | AT haozhang applyingsoftwaremetricstornnforearlyreliabilityevaluation AT jiezhang applyingsoftwaremetricstornnforearlyreliabilityevaluation AT keshi applyingsoftwaremetricstornnforearlyreliabilityevaluation AT huiwang applyingsoftwaremetricstornnforearlyreliabilityevaluation |