A study of subgroup discovery approaches for defect prediction

Context: Although many papers have been published on software defect prediction techniques, machine learning approaches have yet to be fully explored. Objective: In this paper we suggest using a descriptive approach for defect prediction rather than the pre-cise classification techniques that are usually adopted. This allows us to characterise defective modules with simple rules that can easily be applied by practitioners and deliver a practical (or engineering) approach rather than a highly accurate result. Method: We describe two well-known subgroup discovery algorithms, the SD algorithm and the CN2-SD algorithm to obtain rules that identify defect prone modules. The empirical work is performed with pub-licly available datasets from the Promise repository and object-oriented metrics from an Eclipse reposi-tory related to defect prediction. Subgroup discovery algorithms mitigate against characteristics of datasets that hinder the applicability of classification algorithms and so remove the need for preprocess-ing techniques. Results: The results show that the generated rules can be used to guide testing effort in order to improve the quality of software development projects. Such rules can indicate metrics, their threshold values and relationships between metrics of defective modules. Conclusions: The induced rules are simple to use and easy to understand as they provide a description rather than a complete classification of the whole dataset. Thus this paper represents an engineering approach to defect prediction, i.e., an approach which is useful in practice, easily understandable and can be applied by practitioners.ICEBERG IAPP-2012-324356MICINN TIN2011-28956-C02-0

Enhance Rule Based Detection for Software Fault Prone Modules

Software quality assurance is necessary to increase the level of confidence in the developed software and reduce the overall cost for developing software projects. The problem addressed in this research is the prediction of fault prone modules using data mining techniques. Predicting fault prone modules allows the software managers to allocate more testing and resources to such modules. This can also imply a good investment in better design in future systems to avoid building error prone modules. Software quality models that are based upon data mining from previous projects can identify fault-prone modules in the current similar development project, once similarity between projects is established. In this paper, we applied different data mining rule-based classification techniques on several publicly available datasets of the NASA software repository (e.g. PC1, PC2, etc). The goal was to classify the software modules into either fault prone or not fault prone modules. The paper proposed a modification on the RIDOR algorithm on which the results show that the enhanced RIDOR algorithm is better than other classification techniques in terms of the number of extracted rules and accuracy. The implemented algorithm learns defect prediction using mining static code attributes. Those attributes are then used to present a new defect predictor with high accuracy and low error rate

Searching for rules to detect defective modules: A subgroup discovery approach

Data mining methods in software engineering are becoming increasingly important as they can support several aspects of the software development life-cycle such as quality. In this work, we present a data mining approach to induce rules extracted from static software metrics characterising fault-prone modules. Due to the special characteristics of the defect prediction data (imbalanced, inconsistency, redundancy) not all classification algorithms are capable of dealing with this task conveniently. To deal with these problems, Subgroup Discovery (SD) algorithms can be used to find groups of statistically different data given a property of interest. We propose EDER-SD (Evolutionary Decision Rules for Subgroup Discovery), a SD algorithm based on evolutionary computation that induces rules describing only fault-prone modules. The rules are a well-known model representation that can be easily understood and applied by project managers and quality engineers. Thus, rules can help them to develop software systems that can be justifiably trusted. Contrary to other approaches in SD, our algorithm has the advantage of working with continuous variables as the conditions of the rules are defined using intervals. We describe the rules obtained by applying our algorithm to seven publicly available datasets from the PROMISE repository showing that they are capable of characterising subgroups of fault-prone modules. We also compare our results with three other well known SD algorithms and the EDER-SD algorithm performs well in most cases.Ministerio de Educación y Ciencia TIN2007-68084-C02-00Ministerio de Educación y Ciencia TIN2010-21715-C02-0

A paired learner-based approach for concept drift detection and adaptation in software defect prediction

The early and accurate prediction of defects helps in testing software and therefore leads to an overall higher-quality product. Due to drift in software defect data, prediction model performances may degrade over time. Very few earlier works have investigated the significance of concept drift (CD) in software-defect prediction (SDP). Their results have shown that CD is present in software defect data and tha it has a significant impact on the performance of defect prediction. Motivated from this observation, this paper presents a paired learner-based drift detection and adaptation approach in SDP that dynamically adapts the varying concepts by updating one of the learners in pair. For a given defect dataset, a subset of data modules is analyzed at a time by both learners based on their learning experience from the past. A difference in accuracies of the two is used to detect drift in the data. We perform an evaluation of the presented study using defect datasets collected from the SEACraft and PROMISE data repositories. The experimentation results show that the presented approach successfully detects the concept drift points and performs better compared to existing methods, as is evident from the comparative analysis performed using various performance parameters such as number of drift points, ROC-AUC score, accuracy, and statistical analysis using Wilcoxon signed rank test. Keywords: concept drift; naive Bayes; random forest; software defect prediction; software quality assurance.publishedVersio

Absolute Correlation Weighted Naïve Bayes for Software Defect Prediction

The maintenance phase of the software project can be very expensive for the developer team and harmful to the users because some flawed software modules. It can be avoided by detecting defects as early as possible. Software defect prediction will provide an opportunity for the developer team to test modules or files that have a high probability defect. Naïve Bayes has been used to predict software defects. However, Naive Bayes assumes all attributes are equally important and are not related each other while, in fact, this assumption is not true in many cases. Absolute value of correlation coefficient has been proposed as weighting method to overcome Naïve Bayes assumptions. In this study, Absolute Correlation Weighted Naïve Bayes have been proposed. The results of parametric test on experiment results show that the proposed method improve the performance of Naïve Bayes for classifying defect-prone on software defect prediction

A methodology for the generation of efficient error detection mechanisms

A dependable software system must contain error detection mechanisms and error recovery mechanisms. Software components for the detection of errors are typically designed based on a system specification or the experience of software engineers, with their efficiency typically being measured using fault injection and metrics such as coverage and latency. In this paper, we introduce a methodology for the design of highly efficient error detection mechanisms. The proposed methodology combines fault injection analysis and data mining techniques in order to generate predicates for efficient error detection mechanisms. The results presented demonstrate the viability of the methodology as an approach for the development of efficient error detection mechanisms, as the predicates generated yield a true positive rate of almost 100% and a false positive rate very close to 0% for the detection of failure-inducing states. The main advantage of the proposed methodology over current state-of-the-art approaches is that efficient detectors are obtained by design, rather than by using specification-based detector design or the experience of software engineers

High-Dimensional Software Engineering Data and Feature Selection

Software metrics collected during project development play a critical role in software quality assurance. A software practitioner is very keen on learning which software metrics to focus on for software quality prediction. While a concise set of software metrics is often desired, a typical project collects a very large number of metrics. Minimal attention has been devoted to finding the minimum set of software metrics that have the same predictive capability as a larger set of metrics – we strive to answer that question in this paper. We present a comprehensive comparison between seven commonly-used filter-based feature ranking techniques (FRT) and our proposed hybrid feature selection (HFS) technique. Our case study consists of a very highdimensional (42 software attributes) software measurement data set obtained from a large telecommunications system. The empirical analysis indicates that HFS performs better than FRT; however, the Kolmogorov-Smirnov feature ranking technique demonstrates competitive performance. For the telecommunications system, it is found that only 10% of the software attributes are sufficient for effective software quality prediction

Predicting software faults in large space systems using machine learning techniques

Recently, the use of machine learning (ML) algorithms has proven to be of great practical value in solving a variety of engineering problems including the prediction of failure, fault, and defect-proneness as the space system software becomes complex. One of the most active areas of recent research in ML has been the use of ensemble classifiers. How ML techniques (or classifiers) could be used to predict software faults in space systems, including many aerospace systems is shown, and further use ensemble individual classifiers by having them vote for the most popular class to improve system software fault-proneness prediction. Benchmarking results on four NASA public datasets show the Naive Bayes classifier as more robust software fault prediction while most ensembles with a decision tree classifier as one of its components achieve higher accuracy rates