Estimation of reliability and the number of faults present in software in its early development phase, i.e., requirement analysis or design phase is very beneficial for developing reliable software with optimal cost. Software reliability prediction in early phase of development is highly desirable to the stake holders, software developers, managers and end users. Since, the failure data are unavailable in early phase of software development, different reliability relevant software metrics and similar project data are used to develop models for early software fault prediction. The proposed model uses the linguistic values of software metrics in fuzzy inference system to predict the total number of faults present in software in its requirement analysis phase. Considering specific target reliability, weightage of each input software metrics and size of software, an algorithm has been proposed here for developing general fuzzy rule base. For model validation of the proposed model, 20 real software project data have been used here. The linguistic values from four software metrics related to requirement analysis phase have been considered as model inputs. The performance of the proposed model has been compared with two existing early software fault prediction models.
In this study, a fuzzy logic-based framework has been proposed to predict the situation of the software modules in the earlier phases of software lifecycle. The proposed model has taken into account domain experts' opinions and the available state of different software metrics as inputs. On the basis of dependability measures of software, different modules have been ranked earlier in the development process. Effect of the modules on the reliability, security, and availability of software has been judged by the proposed technique based on Mahalanobis distance metric. The study of software dependability in early phase assists the software developers to take corrective actions, which leads to minimize the testing efforts as well as development time. The proposed technique has been implemented on the promise software engineering repository data set. Performance of the proposed methodology is promising in identifying the fault-prone software modules. The result has also been compared with some known methodologies.
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