A Review of Metrics and Modeling Techniques in Software Fault Prediction Model Development

This paper surveys different software fault predictions progressed through different data analytic techniques reported in the software engineering literature. This study split in three broad areas; (a) The description of software metrics suites reported and validated in the literature. (b) A brief outline of previous research published in the development of software fault prediction model based on various analytic techniques. This utilizes the taxonomy of analytic techniques while summarizing published research. (c) A review of the advantages of using the combination of metrics. Though, this area is comparatively new and needs more research efforts

Adaptive Genetic Algorithm Based Artificial Neural Network for Software Defect Prediction

To meet the requirement of an efficient software defect prediction,in this paper an evolutionary computing based neural network learning scheme has been developed that alleviates the existing Artificial Neural Network (ANN) limitations such as local minima and convergence issues. To achieve optimal software defect prediction, in this paper, Adaptive-Genetic Algorithm (A-GA) based ANN learning and weightestimation scheme has been developed. Unlike conventional GA, in this paper we have used adaptive crossover and mutation probability parameter that alleviates the issue of disruption towards optimal solution. We have used object oriented software metrics, CK metrics for fault prediction and the proposed Evolutionary Computing Based Hybrid Neural Network (HENN)algorithm has been examined for performance in terms of accuracy, precision, recall, F-measure, completeness etc, where it has performed better as compared to major existing schemes. The proposed scheme exhibited 97.99% prediction accuracy while ensuring optimal precision, Fmeasure and recall

Insights on Research Techniques towards Cost Estimation in Software Design

Software cost estimation is of the most challenging task in project management in order to ensuring smoother development operation and target achievement. There has been evolution of various standards tools and techniques for cost estimation practiced in the industry at present times. However, it was never investigated about the overall picturization of effectiveness of such techniques till date. This paper initiates its contribution by presenting taxonomies of conventional cost-estimation techniques and then investigates the research trends towards frequently addressed problems in it. The paper also reviews the existing techniques in well-structured manner in order to highlight the problems addressed, techniques used, advantages associated and limitation explored from literatures. Finally, we also brief the explored open research issues as an added contribution to this manuscript

Predicting Fault-prone Software Module Using Data Mining Technique and Fuzzy Logic

This paper discusses a new model towards reliability and quality improvement of software systems by predicting fault-prone module before testing. Model utilizes the classification capability of data mining techniques and knowledge stored in software metrics to classify the software module as fault-prone or not fault-prone. A decision tree is constructed using ID3 algorithm for existing project data in order to gain information for the purpose of decision making whether a particular module id fault-prone or not. The gained information is converted into fuzzy rules and integrated with fuzzy inference system to predict fault-prone or not fault-prone software module for target data. The model is also able to predict fault-proneness degree of faulty module. The goal is to help software manager to concentrate their testing efforts to fault-prone modules in order to improve the reliability and quality of the software system. We used NASA projects data set from the PROMOSE repository to validate the predictive accuracy of the model

Software defect prediction using Bayesian networks

There are lots of different software metrics discovered and used for defect prediction in the literature. Instead of dealing with so many metrics, it would be practical and easy if we could determine the set of metrics that are most important and focus on them more to predict defectiveness. We use Bayesian networks to determine the probabilistic influential relationships among software metrics and defect proneness. In addition to the metrics used in Promise data repository, we define two more metrics, i.e. NOD for the number of developers and LOCQ for the source code quality. We extract these metrics by inspecting the source code repositories of the selected Promise data repository data sets. At the end of our modeling, we learn the marginal defect proneness probability of the whole software system, the set of most effective metrics, and the influential relationships among metrics and defectiveness. Our experiments on nine open source Promise data repository data sets show that response for class (RFC), lines of code (LOC), and lack of coding quality (LOCQ) are the most effective metrics whereas coupling between objects (CBO), weighted method per class (WMC), and lack of cohesion of methods (LCOM) are less effective metrics on defect proneness. Furthermore, number of children (NOC) and depth of inheritance tree (DIT) have very limited effect and are untrustworthy. On the other hand, based on the experiments on Poi, Tomcat, and Xalan data sets, we observe that there is a positive correlation between the number of developers (NOD) and the level of defectiveness. However, further investigation involving a greater number of projects is needed to confirm our findings.Publisher's VersionAuthor Pre-Prin