Improved Text Mining Algorithm for Fault Detection using Combined D-Matrix

Systematic diagnostic version of Fault dependency (D-matrix) mostly use for setup the fault method records and its contributing courting on the classified system-degree. It includes dependencies and association between recognizable failure approaches and signs and symptoms related to a machine. Proposed system in this paper describes an relations of domain primarily based textual content repository for construction and renovate combined data dependency matrix through mining lacks of the tuple exact unstructured text ,cumulative during the analysis incidents. Here paradigm is combined D matrix and then fault analysis through textual content mining using advance data preprocessing technique approach to pick out dependencies. Using real-existence statistics accumulated and validated in proposed method

Dependency Model-Based Multiple Fault Diagnosis Using Knowledge of Test Result and Fault Prior Probability

Aiming at solving the multiple fault diagnosis problem as well as the sequence of all the potential multiple faults simultaneously, a new multiple fault diagnosis method based on the dependency model method as well as the knowledge in test results and the prior probability of each fault type is proposed. Firstly, the dependency model of the system can be built and used to formulate the fault-test dependency matrix. Then, the dependency matrix is simplified according to the knowledge in the test results of the system. After that, the logic ‘OR’ operation is performed on the feature vectors of the fault status in the simplified dependency matrix to formulate the multiple fault dependency matrix. Finally, fault diagnosis is based on the multiple fault dependency matrix and the ranking of each fault type calculated basing on the prior probability of each fault status. An illustrative numerical example and a case study are presented to verify the effectiveness and superiority of the proposed method.

Evolutionary Cost Cognizant Regression Test Prioritization for Object-Oriented Programs Based on Fault Dependency

Regression testing performed to provide confidence on the newly or updated software system is a resource consuming process. To ease this process, various techniques are developed. One such technique, test case prioritization, orders test cases with respect to the goals such that the most important test case in achieving those goals is scheduled earlier during the testing session. Among such performance goals, the rate of faults detections, measure how faults are detected quickly throughout the regression testing process. Improved dependency detection among faults provides faster feedback to the developers which gives chance to debug leading faults earlier in time. One other goal, the rate of fault severity detection, measure how fast severe fault can be detected in the testing process. Although, previous works address these issues but assumed that the costs of executing test cases and severities of detected faults are the same. However, costs of test and severities of faults varied. Furthermore, they did not consider incorporating evolution process such as applying genetic algorithms to their technique. In this work, we proposed an evolutionary cost-cognizant regression testing approach that prioritizes test case according to the rate of severity detection of test cases connected with dependent faults using genetic algorithms. The aim is to reveal more severe leading faults earlier using least cost in executing the test suite and to measure the efficacy of the technique using APFDc.