A computational model for defect prediction in shape castings based on the interaction of free surface flow, heat transfer, and solidification phenomena

High-integrity castings require sophisticated design and manufacturing procedures to ensure they are essentially macrodefect free. Unfortunately, an important class of such defects—macroporosity, misruns, and pipe shrinkage—are all functions of the interactions of free surface flow, heat transfer, and solidication in complex geometries. Because these defects arise as an interaction of the preceding continuum phenomena, genuinely predictive models of these defects must represent these interactions explicitly. This work describes an attempt to model the formation of macrodefects explicitly as a function of the interacting continuum phenomena in arbitrarily complex three-dimensional geometries. The computational approach exploits a compatible set of finite volume procedures extended to unstructured meshes. The implementation of the model is described together with its testing and a measure of validation. The model demonstrates the potential to predict reliably shrinkage macroporosity, misruns, and pipe shrinkage directly as a result of interactions among free-surface fluid flow, heat transfer, and solidification.

Sample Size vs. Bias in Defect Prediction

Most empirical disciplines promote the reuse and sharing of datasets, as it leads to greater possibility of replication. While this is increasingly the case in Empirical Software Engineering, some of the most popular bug-fix datasets are now known to be biased. This raises two significants concerns: first, that sample bias may lead to underperforming prediction models, and second, that the external validity of the studies based on biased datasets may be suspect. This issue has raised considerable consternation in the ESE literature in recent years. However, there is a confounding factor of these datasets that has not been examined carefully: size. Biased datasets are sampling only some of the data that could be sampled, and doing so in a biased fashion; but biased samples could be smaller, or larger. Smaller data sets in general provide less reliable bases for estimating models, and thus could lead to inferior model performance. In this setting, we ask the question, what affects performance more? bias, or size? We conduct a detailed, large-scale meta-analysis, using simulated datasets sampled with bias from a high-quality dataset which is relatively free of bias. Our results suggest that size always matters just as much bias direction, and in fact much more than bias direction when considering information-retrieval measures such as AUC and F-score. This indicates that at least for prediction models, even when dealing with sampling bias, simply finding larger samples can sometimes be sufficient. Our analysis also exposes the complexity of the bias issue, and raises further issues to be explored in the future.

Contributions to learning Bayesian network models from weakly supervised data: Application to Assisted Reproductive Technologies and Software Defect Classification

162 p.

a dynamic model to handle defect-oriented processes

Tongji Univ, Inst Dev Study, Syst Engn Soc China, Comm Syst Dynam, Syst Dynam Soc, China Chapter, Shanghai Inst Foreign Trade, Syst Dynam Soc, Chapters Asia Pacific Area

data transformation and attribute subset selection: do they help make differences in software failure prediction?

IEEE, IEEE Comp Soc, Tech Council Software Engn

Support vector machines for regression and applications to software quality prediction

Software metrics are the key tool in software quality management. In this paper, we propose to use support vector machines for regression applied to software metrics to predict software quality. In experiments we compare this method with other regression techniques such as Multivariate Linear Regression, Conjunctive Rule and Locally Weighted Regression. Results on benchmark dataset MIS, using mean absolute error, and correlation coefficient as regression performance measures, indicate that support vector machines regression is a promising technique for software quality prediction. In addition, our investigation of PCA based metrics extraction shows that using the first few Principal Components (PC) we can still get relatively good performance.

Adaptive software fault prediction approach using object-oriented metrics

As users continually request additional functionality, software systems will continue to grow in their complexity, as well as in their susceptibility to failures. Particularly for sensitive systems requiring higher levels of reliability, faulty system modules may increase development and maintenance cost. Hence, identifying them early would support the development of reliable systems through improved scheduling and quality control. Research effort to predict software modules likely to contain faults, as a consequence, has been substantial. Although a wide range of fault prediction models have been proposed, we remain far from having reliable tools that can be widely applied to real industrial systems. For projects with known fault histories, numerous research studies show that statistical models can provide reasonable estimates at predicting faulty modules using software metrics. However, as context-specific metrics differ from project to project, the task of predicting across projects is difficult to achieve. Prediction models obtained from one project experience are ineffective in their ability to predict fault-prone modules when applied to other projects. Hence, taking full benefit of the existing work in software development community has been substantially limited. As a step towards solving this problem, in this dissertation we propose a fault prediction approach that exploits existing prediction models, adapting them to improve their ability to predict faulty system modules across different software projects.

Adaptive Software Fault Prediction Approach Using Object-Oriented Metrics

As users continually request additional functionality, software systems will continue to grow in their complexity, as well as in their susceptibility to failures. Particularly for sensitive systems requiring higher levels of reliability, faulty system modules may increase development and maintenance cost. Hence, identifying them early would support the development of reliable systems through improved scheduling and quality control. Research effort to predict software modules likely to contain faults, as a consequence, has been substantial. Although a wide range of fault prediction models have been proposed, we remain far from having reliable tools that can be widely applied to real industrial systems. For projects with known fault histories, numerous research studies show that statistical models can provide reasonable estimates at predicting faulty modules using software metrics. However, as context-specific metrics differ from project to project, the task of predicting across projects is difficult to achieve. Prediction models obtained from one project experience are ineffective in their ability to predict fault-prone modules when applied to other projects. Hence, taking full benefit of the existing work in software development community has been substantially limited. As a step towards solving this problem, in this dissertation we propose a fault prediction approach that exploits existing prediction models, adapting them to improve their ability to predict faulty system modules across different software projects.

软件缺陷预测技术

软件缺陷预测技术从20世纪70年代发展至今,一直是软件工程领域最活跃的内容之一,在分析软件质量、平衡软件成本方面起着重要的作用.研究和讨论了软件缺陷预测技术的起源、发展和当前所面临的挑战,对主流的缺陷预测技术进行了分类讨论和比较,并对典型的软件缺陷的分布模型给出了案例研究.

Avaliação da contribuição de desenvolvedores para projetos de software usando mineração de repositórios de software e mineração de processos

Software Repository Mining (MSR) is a research area that analyses software repositories in order to derive relevant information for the research and practice of software engineering. The main goal of repository mining is to extract static information from repositories (e.g. code repository or change requisition system) into valuable information providing a way to support the decision making of software projects. On the other hand, another research area called Process Mining (PM) aims to find the characteristics of the underlying process of business organizations, supporting the process improvement and documentation. Recent works have been doing several analyses through MSR and PM techniques: (i) to investigate the evolution of software projects; (ii) to understand the real underlying process of a project; and (iii) create defect prediction models. However, few research works have been focusing on analyzing the contributions of software developers by means of MSR and PM techniques. In this context, this dissertation proposes the development of two empirical studies of assessment of the contribution of software developers to an open-source and a commercial project using those techniques. The contributions of developers are assessed through three different perspectives: (i) buggy commits; (ii) the size of commits; and (iii) the most important bugs. For the opensource project 12.827 commits and 8.410 bugs have been analyzed while 4.663 commits and 1.898 bugs have been analyzed for the commercial project. Our results indicate that, for the open source project, the developers classified as core developers have contributed with more buggy commits (although they have contributed with the majority of commits), more code to the project (commit size) and more important bugs solved while the results could not indicate differences with statistical significance between developer groups for the commercial project

predicting upgrade project defects based on enhancement requirements: an empirical study

Chinese Acad Sci, ISCAS Lab Internet Software Technologies

Prediction and Privacy in Healthcare Analytics

Thesis (Master's)--University of Washington, 2016-03

Reporting usability defects-do reporters report what software developers need?

Reporting usability defects can be a challenging task, especially in convincing the software developers that the reported defect actually requires attention. Stronger evidence in the form of specific details is often needed. However, research to date in software defect reporting has not investigated the value of capturing different information based on defect type. We surveyed practitioners in both open source communities and industrial software organizations about their usability defect reporting practices to better understand information needs to address usability defect reporting issues. Our analysis of 147 responses show that reporters often provide observed result, expected result and steps to reproduce when describing usability defects, similar to the way other types of defects are reported. However, reporters rarely provide usability-related information. In fact, reporters ranked cause of the problem is the most difficult information to provide followed by usability principle, video recoding, UI event trace and title. Conversely, software developers consider cause of the problem as the most helpful information for them to fix usability defects. Our statistical analysis reveals a substantial gap between what reporters provide and what software developers need when fixing usability defects. We propose some remedies to resolve this gap.