Identification, Analysis & Empirical Validation (IAV) of Object Oriented Design (OO) Metrics as Quality Indicators

Metrics and Measure are closely inter-related to each other. Measure is defined as way of defining amount, dimension, capacity or size of some attribute of a product in quantitative manner while Metric is unit used for measuring attribute. Software quality is one of the major concerns that need to be addressed and measured. Object oriented (OO) systems require effective metrics to assess quality of software. The paper is designed to identify attributes and measures that can help in determining and affecting quality attributes. The paper conducts empirical study by taking public dataset KC1 from NASA project database. It is validated by applying statistical techniques like correlation analysis and regression analysis. After analysis of data, it is found that metrics SLOC, RFC, WMC and CBO are significant and treated as quality indicators while metrics DIT and NOC are not significant. The results produced from them throws significant impact on improving software quality

Using Negative Binomial Regression Analysis to Predict Software Faults: A Study of Apache Ant

Negative binomial regression has been proposed as an approach to predicting fault-prone software modules. However, little work has been reported to study the strength, weakness, and applicability of this method. In this paper, we present a deep study to investigate the effectiveness of using negative binomial regression to predict fault-prone software modules under two different conditions, self-assessment and forward assessment. The performance of negative binomial regression model is also compared with another popular fault prediction model—binary logistic regression method. The study is performed on six versions of an open-source objected-oriented project, Apache Ant. The study shows (1) the performance of forward assessment is better than or at least as same as the performance of self-assessment; (2) in predicting fault-prone modules, negative binomial regression model could not outperform binary logistic regression model; and (3) negative binomial regression is effective in predicting multiple errors in one modul

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

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

Semi-supervised and Active Learning Models for Software Fault Prediction

As software continues to insinuate itself into nearly every aspect of our life, the quality of software has been an extremely important issue. Software Quality Assurance (SQA) is a process that ensures the development of high-quality software. It concerns the important problem of maintaining, monitoring, and developing quality software. Accurate detection of fault prone components in software projects is one of the most commonly practiced techniques that offer the path to high quality products without excessive assurance expenditures. This type of quality modeling requires the availability of software modules with known fault content developed in similar environment. However, collection of fault data at module level, particularly in new projects, is expensive and time-consuming. Semi-supervised learning and active learning offer solutions to this problem for learning from limited labeled data by utilizing inexpensive unlabeled data.;In this dissertation, we investigate semi-supervised learning and active learning approaches in the software fault prediction problem. The role of base learner in semi-supervised learning is discussed using several state-of-the-art supervised learners. Our results showed that semi-supervised learning with appropriate base learner leads to better performance in fault proneness prediction compared to supervised learning. In addition, incorporating pre-processing technique prior to semi-supervised learning provides a promising direction to further improving the prediction performance. Active learning, sharing the similar idea as semi-supervised learning in utilizing unlabeled data, requires human efforts for labeling fault proneness in its learning process. Empirical results showed that active learning supplemented by dimensionality reduction technique performs better than the supervised learning on release-based data sets

Software fault prediction using object-oriented metrics

Fault-prediction techniques aim to predict the fault prone software modules in order to streamline the effort to be applied in the later phases of software development. Many fault-prediction techniques have been proposed and evaluated for their performance using various performance criteria. However, due to the lack of compiling their performances in proper perspective, one significant issue about the viability of these techniques has not been adequately addressed. In this study, an adaptive cost evaluation framework is proposed that incorporates cost drivers for various fault removal phases, and performs a cost-benefit analysis for the misclassification of faults. Accordingly, our study focuses on investigating two important and related research questions regarding the viability of fault prediction. First, for a given software product, whether performing fault prediction analysis is economically effective or not?. In case of an positive affirmation, then emphasis is provided on how to choose a fault prediction technique for an overall improved performance in terms of cost-effectiveness. In this study, Object-Oriented software metrics have been considered to provide requisite input data to design a classifier using statistical, machine learning and hybrid methods of soft computing. This work, also extends the study on finding the effectiveness of feature reduction techniques. From the obtained results, it is observed that performing fault prediction is quite desirable for those software systems, when the percentage of faulty modules are below the range of certain threshold value