Prediction Of Fault-prone Modules Research Articles

Fault-Prone Module Prediction Approaches Using Identifiers in Source Code

Prediction of fault-prone modules is an important area of software engineering. The authors assumed that the occurrence of faults is related to the semantics in the source code modules. Semantics in a software module can be extracted from identifiers in the module. Identifiers such as variable names and function names in source code are thus essential information to understand code. The naming for identifiers affects on code understandability; thus, the authors expect that they affect software quality. In this study, the authors examine the relationship between the length of identifiers and existence of software faults in a software module. Furthermore, the authors analyze the relationship between occurrence of “words” in identifiers and the existence of faults. From the experiments using the data from open source software, the authors modeled the relationship between the fault occurrence and the length of identifiers, and the relationship between the fault occurrence and the word in identifiers by the random forest technique. The result of the experiment showed that the length of identifiers can predict the fault-proneness of the software modules. Also, the result showed that the word occurrence model is as good a measure as traditional CK and LOC metrics models.

Software Fault Prediction at Design Phase

Prediction of fault-prone modules continues to attract researcher's interest due to its significant impact on software development cost. The most important goal of such techniques is to correctly identify the modules where faults are most likely to present in early phases of software development lifecycle. Various software metrics related to modules level fault data have been successfully used for prediction of fault-prone modules. Goal of this research is to predict the faulty modules at design phase using design metrics of modules and faults related to modules. We have analyzed the effect of pre-processing and different machine learning schemes on eleven projects from NASA Metrics Data Program which offers design metrics and its related faults. Using seven machine learning and four preprocessing techniques we confirmed that models built from design metrics are surprisingly good at fault proneness prediction. The result shows that we should choose Naïve Bayes or Voting feature intervals with discretization for different data sets as they outperformed out of 28 schemes. Naive Bayes and Voting feature intervals has performed AUC > 0.7 on average of eleven projects. Our proposed framework is effective and can predict an acceptable level of fault at design phases.

A metric to detect fault-prone software modules using text filtering

Machine learning approaches have been widely used for fault-prone module detection. Introduction of machine learning approaches induces development of new software metrics for fault-prone module detection. We have proposed an approach to detect fault-prone modules using the spamfiltering technique. To use our approach in the conventional fault-prone module prediction approaches, we construct a metric from the output of spam-filtering based approach. Using our new metric, we conducted an experiment to show the effect of new metric. The result suggested that use of new metric as well as conventional metrics is effective for accuracy of fault-prone module prediction.

Can Faulty Modules Be Predicted by Warning Messages of Static Code Analyzer?

We have proposed a detection method of fault-prone modules based on the spam filtering technique, “Fault-prone filtering.” Fault-prone filtering is a method which uses the text classifier (spam filter) to classify source code modules in software. In this study, we propose an extension to use warning messages of a static code analyzer instead of raw source code. Since such warnings include useful information to detect faults, it is expected to improve the accuracy of fault-prone module prediction. From the result of experiment, it is found that warning messages of a static code analyzer are a good source of fault-prone filtering as the original source code. Moreover, it is discovered that it is more effective than the conventional method (that is, without static code analyzer) to raise the coverage rate of actual faulty modules.

Quality Prediction of Object Oriented Software Using Density Based Clustering Approach

A softw are fault prediction is a proven technique in achieving high software reliability. Prediction of fault-prone modules provides one way to support software quality engineering through improved scheduling and project control. Quality of software is increasingly important and testing related issues are becoming crucial for software. This necessitates the need to develop a real-time assessment technique that classifies these dynamically generated systems as being faulty/fault-free. A variety of software fault predictions techniques have been proposed, but none has proven to be consistently accurate. These techniques include statistical method, machine learning methods, parametric models and mixed algorithms. Therefore, there is a need to find the best techniques for Quality prediction of the software systems by finding the fault proneness. In this study, the performance of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is evaluated for Java based Object Oriented Software system from NASA Metrics Data Program (MDP) data repository on the basis of fault proneness of the classes.

Application of multivariate analysis for software fault prediction

Prediction of fault-prone modules provides one way to support software quality engineering through improved scheduling and project control. The primary goal of our research was to develop and refine techniques for early prediction of fault-prone modules. The objective of this paper is to review and improve an approach previously examined in the literature for building prediction models, i.e. principal component analysis (PCA) and discriminant analysis (DA). We present findings of an empirical study at Ericsson Telecom AB for which the previous approach was found inadequate for predicting the most fault-prone modules using software design metrics. Instead of dividing modules into fault-prone and not-fault-prone, modules are categorized into several groups according to the ordered number of faults. It is shown that the first discriminant coordinates (DC) statistically increase with the ordering of modules, thus improving prediction and prioritization efforts. The authors also experienced problems with the smoothing parameter as used previously for DA. To correct this problem and further improve predictability, separate estimation of the smoothing parameter is shown to be required.