A conceptual Bayesian net model for integrated software quality prediction

Software quality can be described by a set of features, such as functionality, reliability, usability, efficiency, maintainability, portability and others. There are various models for software quality prediction developed in the past. Unfortunately, they typically focus on a single quality feature. The main goal of this study is to develop a predictive model that integrates several features of software quality, including relationships between them. This model is an expert-driven Bayesian net, which can be used in diverse analyses and simulations. The paper discusses model structure, behaviour, calibration and enhancement options as well as possible use in fields other than software engineering

A Life Cycle Software Quality Model Using Bayesian Belief Networks

Software practitioners lack a consistent approach to assessing and predicting quality within their products. This research proposes a software quality model that accounts for the influences of development team skill/experience, process maturity, and problem complexity throughout the software engineering life cycle. The model is structured using Bayesian Belief Networks and, unlike previous efforts, uses widely-accepted software engineering standards and in-use industry techniques to quantify the indicators and measures of software quality. Data from 28 software engineering projects was acquired for this study, and was used for validation and comparison of the presented software quality models. Three Bayesian model structures are explored and the structure with the highest performance in terms of accuracy of fit and predictive validity is reported. In addition, the Bayesian Belief Networks are compared to both Least Squares Regression and Neural Networks in order to identify the technique is best suited to modeling software product quality. The results indicate that Bayesian Belief Networks outperform both Least Squares Regression and Neural Networks in terms of producing modeled software quality variables that fit the distribution of actual software quality values, and in accurately forecasting 25 different indicators of software quality. Between the Bayesian model structures, the simplest structure, which relates software quality variables to their correlated causal factors, was found to be the most effective in modeling software quality. In addition, the results reveal that the collective skill and experience of the development team, over process maturity or problem complexity, has the most significant impact on the quality of software products

An Innovative Approach for Predicting Software Defects by Handling Class Imbalance Problem

From last decade unbalanced data has gained attention as a major challenge for enhancing software quality and reliability. Due to evolution in advanced software development tools and processes, today’s developed software product is much larger and complicated in nature. The software business faces a major issue in maintaining software performance and efficiency as well as cost of handling software issues after deployment of software product. The effectiveness of defect prediction model has been hampered by unbalanced data in terms of data analysis, biased result, model accuracy and decision making. Predicting defects before they affect your software product is one way to cut costs required to maintain software quality. In this study we are proposing model using two level approach for class imbalance problem which will enhance accuracy of prediction model. In the first level, model will balance predictive class at data level by applying sampling method. Second level we will use Random Forest machine learning approach which will create strong classifier for software defect. Hence, we can enhance software defect prediction model accuracy by handling class imbalance issue at data and algorithm level

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

Acquiring and sharing tacit knowledge in software development teams: An empirical study

Context: Sharing expert knowledge is a key process in developing software products. Since expert knowledge is mostly tacit, the acquisition and sharing of tacit knowledge along with the development of a transactive memory system (TMS) are significant factors in effective software teams. Objective: We seek to enhance our understanding human factors in the software development process and provide support for the agile approach, particularly in its advocacy of social interaction, by answering two questions: How do software development teams acquire and share tacit knowledge? What roles do tacit knowledge and transactive memory play in successful team performance? Method: A theoretical model describing the process for acquiring and sharing tacit knowledge and development of a TMS through social interaction is presented and a second predictive model addresses the two research questions above. The elements of the predictive model and other demographic variables were incorporated into a larger online survey for software development teams, completed by 46 software SMEs, consisting of 181 individual team members. Results: Our results show that team tacit knowledge is acquired and shared directly through good quality social interactions and through the development of a TMS with quality of social interaction playing a greater role than transactive memory. Both TMS and team tacit knowledge predict effectiveness but not efficiency in software teams. Conclusion: It is concluded that TMS and team tacit knowledge can differentiate between low- and high-performing teams in terms of effectiveness, where more effective teams have a competitive advantage in developing new products and bringing them to market. As face-to-face social interaction is key, collocated, functionally rich, domain expert teams are advocated rather than distributed teams, though arguably the team manager may be in a separate geographic location provided that there is frequent communication and effective use of issue tracking tools as in agile teams

Measurement and Prediction of Software Performance by Models

Software Performance Engineering (SPE) provides a systematic, quantitative approach to constructing software systems that meet performance objectives. It prescribes ways to build performance into new systems rather than try to fix them later. Performance is a pervasive quality of software systems; everything affects it, from the software itself to all underlying layers, such as operating system, middleware, hardware, communication networks, etc. Software Perfor - mance Engineering encompasses efforts to describe and improve performance, with two distinct approaches: an earlycycle predictive model-based approach, and a late-cycle measurement-based approach. Current progress and future trends within these two approaches are described, with a tendency (and a need) for them to converge, in order to cover the entire development cycle

Predicting Software Reliability Using Ant Colony Optimization Technique with Travelling Salesman Problem for Software Process – A Literature Survey

Computer software has become an essential and important foundation in several versatile domains including medicine, engineering, etc. Consequently, with such widespread application of software, there is a need of ensuring software reliability and quality. In order to measure such software reliability and quality, one must wait until the software is implemented, tested and put for usage for a certain time period. Several software metrics have been proposed in the literature to avoid this lengthy and costly process, and they proved to be a good means of estimating software reliability. For this purpose, software reliability prediction models are built. Software reliability is one of the important software quality features. Software reliability is defined as the probability with which the software will operate without any failure for a specific period of time in a specified environment. Software reliability, when estimated in early phases of software development life cycle, saves lot of money and time as it prevents spending huge amount of money on fixing of defects in the software after it has been deployed to the client. Software reliability prediction is very challenging in starting phases of life cycle model. Software reliability estimation has thus become an important research area as every organization aims to produce reliable software, with good quality and error or defect free software. There are many software reliability growth models that are used to assess or predict the reliability of the software. These models help in developing robust and fault tolerant systems. In the past few years many software reliability models have been proposed for assessing reliability of software but developing accurate reliability prediction models is difficult due to the recurrent or frequent changes in data in the domain of software engineering. As a result, the software reliability prediction models built on one dataset show a significant decrease in their accuracy when they are used with new data. The main aim of this paper is to introduce a new approach that optimizes the accuracy of software reliability predictive models when used with raw data. Ant Colony Optimization Technique (ACOT) is proposed to predict software reliability based on data collected from literature. An ant colony system by combining with Travelling Sales Problem (TSP) algorithm has been used, which has been changed by implementing different algorithms and extra functionality, in an attempt to achieve better software reliability results with new data for software process. The intellectual behavior of the ant colony framework by means of a colony of cooperating artificial ants are resulting in very promising results. Keywords: Software Reliability, Reliability predictive Models, Bio-inspired Computing, Ant Colony Optimization technique, Ant Colon

Predictive Analytics and Software Defect Severity: A Systematic Review and Future Directions

Software testing identifies defects in software products with varying multiplying effects based on their severity levels and sequel to instant rectifications, hence the rate of a research study in the software engineering domain. In this paper, a systematic literature review (SLR) on machine learning-based software defect severity prediction was conducted in the last decade. The SLR was aimed at detecting germane areas central to efficient predictive analytics, which are seldom captured in existing software defect severity prediction reviews. The germane areas include the analysis of techniques or approaches which have a significant influence on the threats to the validity of proposed models, and the bias-variance tradeoff considerations techniques in data science-based approaches. A population, intervention, and outcome model is adopted for better search terms during the literature selection process, and subsequent quality assurance scrutiny yielded fifty-two primary studies. A subsequent thoroughbred systematic review was conducted on the final selected studies to answer eleven main research questions, which uncovers approaches that speak to the aforementioned germane areas of interest. The results indicate that while the machine learning approach is ubiquitous for predicting software defect severity, germane techniques central to better predictive analytics are infrequent in literature. This study is concluded by summarizing prominent study trends in a mind map to stimulate future research in the software engineering industry.publishedVersio