Experience in Predicting Fault-Prone Software Modules Using Complexity Metrics

Complexity metrics have been intensively studied in predicting fault-prone software modules. However, little work is done in studying how to effectively use the complexity metrics and the prediction models under realistic conditions. In this paper, we present a study showing how to utilize the prediction models generated from existing projects to improve the fault detection on other projects. The binary logistic regression method is used in studying publicly available data of five commercial products. Our study shows (1) models generated using more datasets can improve the prediction accuracy but not the recall rate; (2) lowering the cut-off value can improve the recall rate, but the number of false positives will be increased, which will result in higher maintenance effort. We further suggest that in order to improve model prediction efficiency, the selection of source datasets and the determination of cut-off values should be based on specific properties of a project. So far, there are no general rules that have been found and reported to follow

Fault-Proneness Estimation and Java Migration: A Preliminary Case Study

The paper presents and discusses an industrial case study, where an eight year running software project has been analyzed. We collected about 1000 daily-versions, together with the file version control system, and bug tracking data. This project has been migrated from Java 1.4 to Java 1.5, and visible effects of this migration on the bytecode are presented and discussed. From this case study, we expect to observe the effects on the code size produced by the Java technology migration, and to improve the performances of already existing fault-proneness estimation models. Preliminary results about fault-proneness estimation are shown

Evolutionary Computing based an Efficient and Cost Effective Software Defect Prediction System

The earlier defect prediction and fault removal can play a vital role in ensuring software reliability and quality of service In this paper Hybrid Evolutionary computing based Neural Network HENN based software defect prediction model has been developed For HENN an adaptive genetic algorithm A-GA has been developed that alleviates the key existing limitations like local minima and convergence Furthermore the implementation of A-GA enables adaptive crossover and mutation probability selection that strengthens computational efficiency of our proposed system The proposed HENN algorithm has been used for adaptive weight estimation and learning optimization in ANN for defect prediction In addition a novel defect prediction and fault removal cost estimation model has been derived to evaluate the cost effectiveness of the proposed system The simulation results obtained for PROMISE and NASA MDP datasets exhibit the proposed model outperforms Levenberg Marquardt based ANN system LM-ANN and other systems as well And also cost analysis exhibits that the proposed HENN model is approximate 21 66 cost effective as compared to LM-AN

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

Predicting Software Fault Proneness Using Machine Learning

Context: Continuous Integration (CI) is a DevOps technique which is widely used in practice. Studies show that its adoption rates will increase even further. At the same time, it is argued that maintaining product quality requires extensive and time consuming, testing and code reviews. In this context, if not done properly, shorter sprint cycles and agile practices entail higher risk for the quality of the product. It has been reported in literature [68], that lack of proper test strategies, poor test quality and team dependencies are some of the major challenges encountered in continuous integration and deployment. Objective: The objective of this thesis, is to bridge the process discontinuity that exists between development teams and testing teams, due to continuous deployments and shorter sprint cycles, by providing a list of potentially buggy or high risk files, which can be used by testers to prioritize code inspection and testing, reducing thus the time between development and release. Approach: Out approach is based on a five step process. The first step is to select a set of systems, a set of code metrics, a set of repository metrics, and a set of machine learning techniques to consider for training and evaluation purposes. The second step is to devise appropriate client programs to extract and denote information obtained from GitHub repositories and source code analyzers. The third step is to use this information to train the models using the selected machine learning techniques. This step allowed to identify the best performing machine learning techniques out of the initially selected in the first step. The fourth step is to apply the models with a voting classifier (with equal weights) and provide answers to five research questions pertaining to the prediction capability and generality of the obtained fault proneness prediction framework. The fifth step is to select the best performing predictors and apply it to two systems written in a completely different language (C++) in order to evaluate the performance of the predictors in a new environment. Obtained Results: The obtained results indicate that a) The best models were the ones applied on the same system as the one trained on; b) The models trained using repository metrics outperformed the ones trained using code metrics; c) The models trained using code metrics were proven not adequate for predicting fault prone modules; d) The use of machine learning as a tool for building fault-proneness prediction models is promising, but still there is work to be done as the models show weak to moderate prediction capability. Conclusion: This thesis provides insights into how machine learning can be used to predict whether a source code file contains one or more faults that may contribute to a major system failure. The proposed approach is utilizing information extracted both from the system’s source code, such as code metrics, and from a series of DevOps tools, such as bug repositories, version control systems and, testing automation frameworks. The study involved five Java and five Python systems and indicated that machine learning techniques have potential towards building models for alerting developers about failure prone code