Evaluation of Fault Proneness of Modules in Open Source Software Systems Using k-NN Clustering
نویسنده
چکیده
Fault-proneness of a software module is the probability that the module contains faults. A correlation exists between the fault-proneness of the software and the measurable attributes of the code (i.e. the static metrics) and of the testing (i.e. the dynamic metrics). Early detection of fault-prone software components enables verification experts to concentrate their time and resources on the problem areas of the software system under development. This paper introduces the evaluation of the fault proneness of modules in open source software system using k-NN clustering algorithm with Object-Oriented metrics and ck metrics. The contribution of this paper is that it has used Metric values of JEdit open source software for generation of the rules for the classification of software modules in the categories of Faulty and non faulty modules and thereafter empirically validation is performed. The results shows that algorithm approach can be used for finding the fault proneness in object oriented software components.
منابع مشابه
A K-Means Based Clustering Approach for Finding Faulty Modules in Open Source Software Systems
Prediction of fault-prone modules provides one way to support software quality engineering. Clustering is used to determine the intrinsic grouping in a set of unlabeled data. Among various clustering techniques available in literature K-Means clustering approach is most widely being used. This paper introduces K-Means based Clustering approach for software finding the fault proneness of the Obj...
متن کاملEvaluation of Classifiers in Software Fault-Proneness Prediction
Reliability of software counts on its fault-prone modules. This means that the less software consists of fault-prone units the more we may trust it. Therefore, if we are able to predict the number of fault-prone modules of software, it will be possible to judge the software reliability. In predicting software fault-prone modules, one of the contributing features is software metric by which one ...
متن کاملAn Approach to Early Fault Prediction in Software Systems Using K- Means Clustering
Quality of a software component can be measured in terms of fault proneness of data. Quality estimations are made using fault proneness data available from previously developed similar type of projects and the training data consisting of software measurements. To predict faulty modules in software data different techniques have been proposed which includes statistical method, machine learning m...
متن کاملA Hybrid Fault-Proneness Detection Approach Using Text Filtering and Static Code Analysis
We have proposed a fault-prone software module detection method using text-filtering approach, called Fault-proneness filtering. Even though the fault-proneness filtering achieved high accuracy in detecting fault-prone modules, the detail of each fault cannot be specified enough. We thus try to complete such weakness of the fault-proneness filtering by using static code analysis. To do so, we a...
متن کاملAssessment of Change Request Artifacts Impact towards Fault Proneness
Exploring the impact of change requests applied on a software maintenance project helps to forecasts the fault-proneness of the change request to be handled further, which is either a bug fix or a new feature request. In practice the major development community stores change requests and related data using bug tracking systems such as bugzilla. These data, together with the data stored in a ver...
متن کامل