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 Review of Metrics and Modeling Techniques in Software Fault Prediction Model Development

This paper surveys different software fault predictions progressed through different data analytic techniques reported in the software engineering literature. This study split in three broad areas; (a) The description of software metrics suites reported and validated in the literature. (b) A brief outline of previous research published in the development of software fault prediction model based on various analytic techniques. This utilizes the taxonomy of analytic techniques while summarizing published research. (c) A review of the advantages of using the combination of metrics. Though, this area is comparatively new and needs more research efforts

Risk Management for Enterprise Resource Planning System Implementations in Project-Based Firms

Enterprise Resource Planning (ERP) systems have been regarded as one of the most important information technology developments in the past decades. While ERP systems provide the potential to bring substantial benefits, their implementations are characterized with large capital outlay, long duration, and high risks of failure including implementation process failure and system usage failure. As a result, the adoption of ERP systems in project-based firms has been lagged behind lots of companies in many other industries. In order to ensure the success of ERP system implementations in project-based firms, sound risk management is the key. The overall objective of this research is to identify the risks in ERP system implementations within project-based firms and develop a new approach to analyze these risks and quantitatively assess their impacts on ERP system implementation failure. At first, the research describes ERP systems in conjunction with the nature and working practices of project-based firms and current status and issues related to ERP adoption in such firms, and thus analyzes the causes for their relatively low ERP adoption and states the research problems and objectives. Accordingly, a conceptual research framework is presented, and the procedures and research methods are outlined. Secondly, based on the risk factors regarding generic ERP projects in extant literature, the research comprehensively identifies the risk factors of ERP system implementation within project-based firms. These risk factors are classified into different categories, qualitatively described and analyzed, and used to establish a risk taxonomy. Thirdly, an approach is developed based on fault tree analysis to decompose ERP systems failure and assess the relationships between ERP component failures and system usage failure, both qualitatively and quantitatively. The principles and processes of this approach and related fault tree analysis methods and techniques are presented in the context of ERP projects. Fourthly, certain practical strategies are proposed to manage the risks of ERP system implementations. The proposed risk assessment approach and management strategies together with the comprehensive list of identified risk factors not only contribute to the body of knowledge of information system risk management, but also can be used as an effective tool by practitioners to actively analyze, assess, and manage the risks of ERP system implementations within project-based firms

Evolutionary Computing based an Efficient and Cost Effective Software Defect Prediction System

The earlier defect prediction and fault removal can play a vital role in ensuring software reliability and quality of service In this paper Hybrid Evolutionary computing based Neural Network HENN based software defect prediction model has been developed For HENN an adaptive genetic algorithm A-GA has been developed that alleviates the key existing limitations like local minima and convergence Furthermore the implementation of A-GA enables adaptive crossover and mutation probability selection that strengthens computational efficiency of our proposed system The proposed HENN algorithm has been used for adaptive weight estimation and learning optimization in ANN for defect prediction In addition a novel defect prediction and fault removal cost estimation model has been derived to evaluate the cost effectiveness of the proposed system The simulation results obtained for PROMISE and NASA MDP datasets exhibit the proposed model outperforms Levenberg Marquardt based ANN system LM-ANN and other systems as well And also cost analysis exhibits that the proposed HENN model is approximate 21 66 cost effective as compared to LM-AN

Is It Safe to Uplift This Patch? An Empirical Study on Mozilla Firefox

In rapid release development processes, patches that fix critical issues, or implement high-value features are often promoted directly from the development channel to a stabilization channel, potentially skipping one or more stabilization channels. This practice is called patch uplift. Patch uplift is risky, because patches that are rushed through the stabilization phase can end up introducing regressions in the code. This paper examines patch uplift operations at Mozilla, with the aim to identify the characteristics of uplifted patches that introduce regressions. Through statistical and manual analyses, we quantitatively and qualitatively investigate the reasons behind patch uplift decisions and the characteristics of uplifted patches that introduced regressions. Additionally, we interviewed three Mozilla release managers to understand organizational factors that affect patch uplift decisions and outcomes. Results show that most patches are uplifted because of a wrong functionality or a crash. Uplifted patches that lead to faults tend to have larger patch size, and most of the faults are due to semantic or memory errors in the patches. Also, release managers are more inclined to accept patch uplift requests that concern certain specific components, and-or that are submitted by certain specific developers.Comment: In proceedings of the 33rd International Conference on Software Maintenance and Evolution (ICSME 2017

FAULT LINKS: IDENTIFYING MODULE AND FAULT TYPES AND THEIR RELATIONSHIP

The presented research resulted in a generic component taxonomy, a generic code-faulttaxonomy, and an approach to tailoring the generic taxonomies into domain-specific aswell as project-specific taxonomies. Also, a means to identify fault links was developed.Fault links represent relationships between the types of code-faults and the types ofcomponents being developed or modified. For example, a fault link has been found toexist between Controller modules (that forms a backbone for any software via. itsdecision making characteristics) and Control/Logic faults (such as unreachable code).The existence of such fault links can be used to guide code reviews, walkthroughs, testingof new code development, as well as code maintenance. It can also be used to direct faultseeding. The results of these methods have been validated. Finally, we also verified theusefulness of the obtained fault links through an experiment conducted using graduatestudents. The results were encouraging

A FRAMEWORK FOR SOFTWARE RELIABILITY MANAGEMENT BASED ON THE SOFTWARE DEVELOPMENT PROFILE MODEL

Recent empirical studies of software have shown a strong correlation between change history of files and their fault-proneness. Statistical data analysis techniques, such as regression analysis, have been applied to validate this finding. While these regression-based models show a correlation between selected software attributes and defect-proneness, in most cases, they are inadequate in terms of demonstrating causality. For this reason, we introduce the Software Development Profile Model (SDPM) as a causal model for identifying defect-prone software artifacts based on their change history and software development activities. The SDPM is based on the assumption that human error during software development is the sole cause for defects leading to software failures. The SDPM assumes that when a software construct is touched, it has a chance to become defective. Software development activities such as inspection, testing, and rework further affect the remaining number of software defects. Under this assumption, the SDPM estimates the defect content of software artifacts based on software change history and software development activities. SDPM is an improvement over existing defect estimation models because it not only uses evidence from current project to estimate defect content, it also allows software managers to manage software projects quantitatively by making risk informed decisions early in software development life cycle. We apply the SDPM in several real life software development projects, showing how it is used and analyzing its accuracy in predicting defect-prone files and compare the results with the Poisson regression model

Teaching Model for Disaster Preparedness School Based Earthquake Prone Earthquake in Lombok

The problem in this research is that the teaching of school-based earthquake disaster preparedness in Lombok has not been optimal. In fact, the island of Lombok is an area with a high level of vulnerability to earthquakes. This is because one of them is the Flores Thrust which stretches from the eastern tip of the Flores Sea to the north of Bali. Thus in this study the main objective is to develop a preparedness teaching model for earthquake-prone schools. Where earthquake disaster preparedness is all efforts and activities carried out before a natural disaster occurs, during a disaster and immediately after a disaster to quickly and effectively respond to the situation or situation. The research method used is a research and development approach (Research & Development). The results showed several results including; First, the use of this preparedness teaching model shows that schools have more knowledge as a result of modeling in learning; Second, the exposure of the functions and responsibilities of one of the main leading sectors in disaster management; and The three resulting teaching models have simplified disaster management in schools because they are integrated with Social Science learning so that they are easily realized. So with the model of teaching student preparedness it will be more effective and efficient in order to improve their ability to face earthquakes that can occur at any time