Software testing is an area where software products are examined through a series of verification and validation processes respectively. This phase of software development carries out the process of detection and removal of software faults. But this detection and removal of faults together consume up to 60% of project budget (Beizer, 1990). Applying equal testing and verification efforts to all...

Today, resources are geared towards modifying rather than developing new software systems. Changes are necessary during the system’s lifetime to keep it useful but the major challenge is how these changes are controlled and managed. Software systems are complex with large dependency webs and components that are fault-prone. Modifying components without regard to its dependencies or its fault-pr...

Background: The accurate prediction of where faults are likely to occur in code can help direct test effort, reduce costs and improve the quality of software. Objective of this paper is We investigate how the context of models, the independent variables used and the modeling techniques applied, influence the performance of fault prediction models. Method on We used a systematic literature revie...

Machine Learning (ML) approaches have a great impact in fault prediction. Demand for producing quality assured software in an organization has been rapidly increased during the last few years. This leads to increase in development of machine learning algorithms for analyzing and classifying the data sets, which can be used in constructing models for predicting the important quality attributes s...

We propose to validate experimentally a theory of software certification that proceeds from assessment of confidence in fault-freeness (due to standards) to conservative prediction of failure-free operation.

Due to high cost of fixing failures, safety concerns, and legal liabilities, organizations need to produce software that is highly reliable. Software reliability growth models have been developed by software developers in tracking and measuring the growth of reliability. Most of the Software Reliability Growth Models, which have been proposed, treat the event of software fault detection in the ...

Quality of a software component can be measured in terms of fault proneness of data. Quality estimations are made using fault proneness data available from previously developed similar type of projects and the training data consisting of software measurements. To predict faulty modules in software data different techniques have been proposed which includes statistical method, machine learning m...

Feature subset selection is the process of choosing a subset of good features with respect to the target concept. A clustering based feature subset selection algorithm has been applied over software defect prediction data sets. Software defect prediction domain has been chosen due to the growing importance of maintaining high reliability and high quality for any software being developed. A soft...

Software systems are normally developed in a number of releases. Each release usually modifies existing code. In this study we show that such modified code can be an important source of faults. Since faults are considered major cost drivers of software projects, the ability to identify fault-prone classes before they are implemented would give a chance to apply some preventive measures, which c...