In this paper we address the development, testing, and evaluation schemes for software reliability, and the integration of these schemes into a unified and consistent paradigm. Specifically, techniques and tools for the three phases of software reliability engineering will be described. The three phases are (1) modeling and analysis, (2) design and implementation, and (3) testing and measuremen...

Empirical validation of software metrics to predict quality using machine learning methods is important to ensure their practical relevance in the software organizations. In this paper, we build a Support Vector Machine (SVM) model to find the relationship between object-oriented metrics given by Chidamber and Kemerer and fault proneness. The proposed model is empirically evaluated using public...

Until now, various techniques have been proposed for predicting fault prone modules based on prediction performance. Unfortunately quality improvement and cost reduction has been rarely assessed. The main motivation here is optimization of acceptance testing to provide high quality services to customers. From this perspective, the primary goal of this proposed methodology is reduction of accept...

This paper presents a new approach for predicting software faults by means of two-level clustering with unknown number of clusters. We employed Self-Organizing Map method and our proposed clustering approach in the first and second level, respectively, to classify historical and development data into clusters. Next we applied the Radial-Basis Function Network to predict software faults occurred...

Identifying and locating defects in software projects is a difficult work. In particular, when project sizes grow, this task becomes expensive. The aim of this research is to establish a method for identifying software defects using data mining applications methods. In this work we used Synthetic data Program (SD).We used mining methods to construct a two step model that predicts potentially de...

It is often wondered why so much of the software engineering research that appears in the literature is not adopted by practitioners. After all, there are lots of exciting new ideas that could potentially improve both the quality and efficiency of software production. So why is this research ignored? Is it just a matter of ignorance or shortsightedness on the part of practitioners? In this pape...

Most of the fault prediction studies have focused on the binary classification models that determine whether the input modules are fault-prone or not. More recently, several studies have shown that severity-based multi-classification models are more useful since they can predict the fault-proneness depending on the severity of the defects in the module. We present new severity-based prediction ...

Software metrics and fault data are collected during the software development cycle. A typical software defect prediction model is trained using this collected data. Therefore the quality and characteristics of the underlying software metrics play an important role in the efficacy of the prediction model. However, superfluous software metrics often exist. Identifying a small subset of metrics b...

—The dynamic software development organizations optimize the usage of resources to deliver the products in the specified time with the fulfilled requirements. This requires prevention or repairing of the faults as quick as possible. In this paper an approach for predicting the run-time errors in java is introduced. The paper is concerned with faults due to inheritance and violation of java cons...

Software defection prediction is not only crucial for improving software quality, but also helpful for software test effort estimation. As is wellknown, 80%of the fault happens in 20%of themodules. Therefore,weneed tofind out themost error pronemodules accurately and correct them in time to save time, money, and energy. Support vector machine (SVM) is an advanced classification method that fits...