Adaptive Genetic Algorithm Based Artificial Neural Network for Software Defect Prediction

To meet the requirement of an efficient software defect prediction,in this paper an evolutionary computing based neural network learning scheme has been developed that alleviates the existing Artificial Neural Network (ANN) limitations such as local minima and convergence issues. To achieve optimal software defect prediction, in this paper, Adaptive-Genetic Algorithm (A-GA) based ANN learning and weightestimation scheme has been developed. Unlike conventional GA, in this paper we have used adaptive crossover and mutation probability parameter that alleviates the issue of disruption towards optimal solution. We have used object oriented software metrics, CK metrics for fault prediction and the proposed Evolutionary Computing Based Hybrid Neural Network (HENN)algorithm has been examined for performance in terms of accuracy, precision, recall, F-measure, completeness etc, where it has performed better as compared to major existing schemes. The proposed scheme exhibited 97.99% prediction accuracy while ensuring optimal precision, Fmeasure and recall

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

The role of Artificial Intelligence in Software Engineering

There has been a recent surge in interest in the application of Artificial Intelligence (AI) techniques to Software Engineering (SE) problems. The work is typified by recent advances in Search Based Software Engineering, but also by long established work in Probabilistic reasoning and machine learning for Software Engineering. This paper explores some of the relationships between these strands of closely related work, arguing that they have much in common and sets out some future challenges in the area of AI for SE. © 2012 IEEE

Why the Virtual Nature of Software Makes It Ideal for Search Based Optimization

This paper(1) provides a motivation for the application of search based optimization to Software Engineering, an area that has come to be known as Search Based Software Engineering (SBSE). SBSE techniques have already been applied to many problems throughout the Software Engineering lifecycle, with new application domains emerging on a regular basis. The approach is very generic and therefore finds wide application in Software Engineering. It facilitates automated and semi-automated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. Previous work has already discussed, in some detail, the advantages of the SBSE approach for Software Engineering. This paper summarises previous work and goes further, by arguing that Software Engineering provides the ideal set of application problems for which optimization algorithms are supremely well suited

The relationship between search based software engineering and predictive modeling

Search Based Software Engineering (SBSE) is an approach to software engineering in which search based optimization algorithms are used to identify optimal or near optimal solutions and to yield insight. SBSE techniques can cater for multiple, possibly competing objectives and/or constraints and applications where the potential solution space is large and complex. This paper will provide a brief overview of SBSE, explaining some of the ways in which it has already been applied to construction of predictive models. There is a mutually beneficial relationship between predictive models and SBSE. The paper sets out eleven open problem areas for Search Based Predictive Modeling and describes how predictive models also have role to play in improving SBSE

A multi-objective evolutionary approach for automatic generation of test cases from state machines

Orientador: Eliane MartinsTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: A geração automática de casos de teste contribui tanto para melhorar a produtividade quanto para reduzir esforço e custo no processo de desenvolvimento de software. Neste trabalho é proposta uma abordagem, denominada MOST (Multi-Objective Search-based Testing approach from EFSM), para gerar casos de teste a partir de Máquina de Estados Finitos Estendida (MEFE) com a aplicação de uma técnica de otimização. No teste baseado em MEFE, é necessário encontrar uma sequência de entrada para exercitar um caminho no modelo, a fim de cobrir um critério de teste (e.g. todas as transições). Como as sequências podem ter diferentes tamanhos, motivou-se o desenvolvimento do algoritmo M-GEOvsl (Multi-Objective Generalized Extremal Optimization with variable string length) que permite gerar soluções de diferentes tamanhos. Além disso, por ser um algoritmo multiobjetivo, M-GEOvsl também possibilita que mais de um critério seja usado para avaliar as soluções. Com a aplicação desse algoritmo em MOST, tanto a cobertura da transição alvo quanto o tamanho da sequência são levados em consideração na geração de casos de teste. Para guiar a busca, são utilizadas informações das dependências do modelo. O algoritmo gera as sequências de entrada, incluindo os valores de seus parâmetros. Em MOST, um modelo executável da MEFE recebe como entrada os dados gerados pelo M-GEOvsl e produz dinamicamente os caminhos percorridos. Uma vez que os aspectos de controle e dados do modelo são considerados durante a execução do modelo, evita-se o problema de geração de caminhos infactíveis. Um caminho pode ser sintaticamente possível, mas semanticamente infactível, devido aos conitos de dados envolvidos no modelo. Para avaliar a abordagem proposta foram realizados vários experimentos com modelos da literatura e de aplicações reais. Os resultados da abordagem também foram comparados com os casos de teste obtidos em um trabalho relacionado.Abstract: Automated test case generation can improve the productivity as well as reduce effort and cost in the software development process. In this work an approach, named MOST (Multi- Objective Search-based Testing approach from EFSM), is proposed to generate test cases from Extended Finite State Machine (EFSM) using an optimization technique. In EFSM based testing, an input sequence should be found to sensitize a path in the model, in order to cover a test criterion (e.g. all transitions). As the sequences can have different lengths, it motivates the development of the M-GEOvsl (Multi-Objective Generalized Extremal Optimization with variable string length) algorithm that makes possible the generation of solutions with different lengths. Moreover, as a multiobjective algorithm, M-GEOvsl also allows to use more than one criterion to evaluate the solutions. Using this algorithm in MOST, the coverage of the target transition as well as the sequence length are taken into account in the test case generation. To guide the search, the information about the model dependences is used. The algorithm generates the input sequences, including the values of their parameters. In MOST, an executable model of the EFSM receives as input the data generated by M-GEOvsl and produces the traversed paths dynamically. Since the control and data aspects are considered during model execution, the problem of infeasible path generation is avoided. A path can be syntatically possible, but semantically infeasible, due to the data conicts in the model. In order to evaluate the proposed approach, experiments were performed with models of the literature and real-world applications. The results were also compared to the test cases obtained in a related workDoutoradoCiência da ComputaçãoDoutor em Ciência da Computaçã

Search Based Software Project Management

This thesis investigates the application of Search Based Software Engineering (SBSE) approach in the field of Software Project Management (SPM). With SBSE approaches, a pool of candidate solutions to an SPM problem is automatically generated and gradually evolved to be increasingly more desirable. The thesis is motivated by the observation from industrial practice that it is much more helpful to the project manager to provide insightful knowledge than exact solutions. We investigate whether SBSE approaches can aid the project managers in decision making by not only providing them with desirable solutions, but also illustrating insightful “what-if” scenarios during the phases of project initiation, planning and enactment. SBSE techniques can automatically “evolve” solutions to software requirement elicitation, project staffing and scheduling problems. However, the current state-of- the-art computer-aided software project management tools remain limited in several aspects. First, software requirement engineering is plagued by problems associated with unreliable estimates. The estimations made early are assumed to be accurate, but the projects are estimated and executed in an environment filled with uncertainties that may lead to delay or disruptions. Second, software project scheduling and staffing are two closely related problems that have been studied separately by most published research in the field of computer aided software project management, but software project managers are usually confronted with the complex trade-off and correlations of scheduling and staffing. Last, full attendance of required staff is usually assumed after the staff have been assigned to the project, but the execution of a project is subject to staff absences because of sickness and turnover, for example. This thesis makes the following main contributions: (1) Introducing an automated SBSE approach to Sensitivity Analysis for requirement elicitation, which helps to achieve more accurate estimations by directing extra estimation effort towards those error-sensitive requirements and budgets. (2) Demonstrating that Co-evolutionary approaches can simultaneously co-evolve solutions for both work package sequencing and project team sizing. The proposed approach to these two interrelated problems yields better results than random and single-population evolutionary algorithms. (3) Presenting co-evolutionary approaches that can guide the project manager to anticipate and ameliorate the impact of staff absence. (4) The investigations of seven sets of real world data on software requirement and software project plans reveal general insights as well as exceptions of our approach in practise. (5) The establishment of a tool that implements the above concepts. These contributions support the thesis that automated SBSE tools can be beneficial to solution generation, and most importantly, insightful knowledge for decision making in the practise of software project management