Moataz A. Ahmed

ADAPTIVE FUZZY LOGIC BASED FRAMEWORK FOR SOFTWARE DEVELOPMENT EFFORT PREDICTION

Abstract Algorithmic effort prediction models are limited by their inability to cope with uncertainties and imprecision present in software projects early in the development life cycle. In this paper, we present an adaptive fuzzy logic framework for software effort prediction. The training and adaptation algorithms implemented in the framework tolerates imprecision, explains prediction rationale through rules, incorporates experts knowledge, offers transparency in the prediction system, and could adapt to new environments as new data becomes available. Our validation experiment was carried out on artificial datasets as well as the COCOMO public database. We also present an experimental validation of the training procedure employed in the framework.

GA-based multiple paths test data generator

Developers have learned over time that software testing costs a considerable amount of a software project budget. Hence, software quality managers have been looking for solutions to reduce testing costs and time. Considering path coverage as the test adequacy criterion, we propose using genetic algorithms (GA) for automating the generation of test data for white-box testing. There are evidences that GA has been already successful in generating test data. However, existing GA-based test data generators suffer from some problems. This paper presents our approach to overcome one of these problems; that is the inefficiency in covering multiple target paths. We have designed a GA-based test data generator that is, in one run, able to synthesize multiple test data to cover multiple target paths. Moreover, we have implemented a set of variations of the generator. Experimental results show that our test data generator is more efficient and more effective than others.

Handling imprecision and uncertainty in software development effort prediction: A type-2 fuzzy logic based framework

Traditional approaches for software projects effort prediction such as the use of mathematical formulae derived from historical data, or the use of experts judgments are plagued with issues pertaining to effectiveness and robustness in their results. These issues are more pronounced when these effort prediction approaches are used during the early phases of the software development lifecycle, for example requirements development, whose effort predictors along with their relationships to effort are characterized as being even more imprecise and uncertain than those of later development phases, for example design. Recent works have demonstrated promising results using approaches based on fuzzy logic. Effort prediction systems that use fuzzy logic can deal with imprecision; they, however, can not deal with uncertainty. This paper presents an effort prediction framework that is based on type-2 fuzzy logic to allow handling imprecision and uncertainty inherent in the information available for effort prediction. Evaluation experiments have shown the framework to be promising.

Genetic algorithm based test data generator

Effective and efficient test data generation is one of the major challenging and time-consuming tasks within the software testing process. Researchers have proposed different methods to generate test data automatically, however, those methods suffer from different drawbacks. In this paper we present a genetic algorithm-based approach that tries to generate a test data that is expected to cover a given set of target paths. Our proposed fitness function is intended to achieve path coverage that incorporates path traversal techniques, neighborhood influence, weighting, and normalization. This integration improves the GA performance in terms of search space exploitation and exploration, and allows faster convergence. We performed some experiments using our proposed approach, where results were promising.

Software development effort prediction: A study on the factors impacting the accuracy of fuzzy logic systems

Reliable effort prediction remains an ongoing challenge to software engineers. Traditional approaches to effort prediction such as the use of models derived from historical data, or the use of expert opinion are plagued with issues pertaining to their effectiveness and robustness. These issues are more pronounced when the effort prediction is used during the early phases of the software development lifecycle. Recent works have demonstrated promising results obtained with the use of fuzzy logic. Fuzzy logic based effort prediction systems can deal better with imprecision, which characterizes the early phases of most software development projects, for example requirements development, whose effort predictors along with their relationships to effort are characterized as being even more imprecise and uncertain than those of later development phases, for example design. Fuzzy logic based prediction systems could produce further better estimates provided that various parameters and factors pertaining to fuzzy logic are carefully set. In this paper, we present an empirical study, which shows that the prediction accuracy of a fuzzy logic based effort prediction system is highly dependent on the system architecture, the corresponding parameters, and the training algorithms.

Probabilistic size proxy for software effort prediction: A framework

Software effort prediction is an important and challenging activity that takes place during the early stages of software development, where costing is needed. Software size estimate is one of the most popular inputs for software effort prediction models. Accordingly, providing a size estimate with good accuracy early in the lifecycle is very important; it is equally challenging too. Estimates that are computed early in the development lifecycle, when it is needed the most, are typically associated with uncertainty. However, none of the prominent software effort prediction techniques or software size metrics addresses this issue satisfactorily. In this paper, we propose a framework for developing probabilistic size proxies for software effort prediction using information from conceptual UML models created early in the software development lifecycle. The framework accounts for uncertainty in software size and effort prediction by providing the estimate as a probability density function instead of a certain value. We conducted a case study using open source datasets and the results were encouraging.

Soft Computing Based Effort Prediction Systems — A Survey

Software development effort prediction is one of the most critical activities in managing software projects. Algorithmic effort prediction models, which have dominated the software engineering community, are limited by their inability to cope with uncertainties and imprecision present in software projects early in the development life cycle. More recently, attention has turned to a variety of machine learning methods, and soft computing in particular to predict software development effort. Soft computing is a consortium of methodologies centering on fuzzy logic (FL), artificial neural networks (ANN), and evolutionary computation (EC). There are evidences that soft computing has been able to address some of the problems associated with previous models. However, there is no clear common ground (criteria) for assessing, classifying, and comparing these different soft computing based effort prediction techniques. In this chapter, we present an evaluation scheme for assessing and comparing soft computing based effort prediction techniques. We present a critical survey of the state-of-the-art application of soft computing in development effort prediction using the set of attributes proposed. Results from our survey reveal that many openings exist for improving soft computing based prediction techniques.

Machine learning approaches for predicting software maintainability: a fuzzy-based transparent model

Software quality is one of the most important factors for assessing the global competitive position of any software company. Thus, the quantification of the quality parameters and integrating them into the quality models is very essential.Many attempts have been made to precisely quantify the software quality parameters using various models such as Boehms Model, McCalls Model and ISO/IEC 9126 Quality Model. A major challenge, although, is that effective quality models should consider two types of knowledge: imprecise linguistic knowledge from the experts and precise numerical knowledge from historical data.Incorporating the experts’ knowledge poses a constraint on the quality model; the model has to be transparent.In this study, the authorspropose a process for developing fuzzy logic-based transparent quality prediction models.They applied the process to a case study where Mamdani fuzzy inference engine is used to predict software maintainability.Theycompared the Mamdani-based model with other machine learning approaches.The resultsshow that the Mamdani-based model is superior to all.

A tool for measuring inheritance coupling in object-oriented systems

Analyzing object-oriented systems in order to evaluate their quality gains its importance as the paradigm continues to increase in popularity. Consequently, several object-oriented metrics have been proposed to evaluate different aspects of these systems such as class coupling. In object-oriented design, three types of coupling may exist between classes: inheritance coupling, interaction coupling, and component coupling. This paper presents a tool for measuring inheritance coupling in object-oriented systems.

Machine Learning-Based Software Quality Prediction Models: State of the Art

Quantification of parameters affecting the software quality is one of the important aspects of research in the field of software engineering. In this paper, we present a comprehensive literature survey of prominent quality molding studies. The survey addresses two views: (1) quantification of parameters affecting the software quality; and (2) using machine learning techniques in predicting the software quality. The paper concludes that, model transparency is a common shortcoming to all the surveyed studies.