Software Defect Association Mining and Defect Correction Effort Prediction

Much current software defect prediction work concentrates on the number of defects remaining in software system. In this paper, we present association rule mining based methods to predict defect associations and defect-correction effort. This is to help developers detect software defects and assist project managers in allocating testing resources more effectively. We applied the proposed methods to the SEL defect data consisting of more than 200 projects over more than 15 years. The results show that for the defect association prediction, the accuracy is very high and the false negative rate is very low. Likewise for the defect-correction effort prediction, the accuracy for both defect isolation effort prediction and defect correction effort prediction are also high. We compared the defect-correction effort prediction method with other types of methods: PART, C4.5, and Na¨ıve Bayes and show that accuracy has been improved by at least 23%. We also evaluated the impact of support and confidence levels on prediction accuracy, false negative rate, false positive rate, and the number of rules. We found that higher support and confidence levels may not result in higher prediction accuracy, and a sufficient number of rules is a precondition for high prediction accuracy

Rcapture: Loglinear Models for Capture-Recapture in R

This article introduces Rcapture, an R package for capture-recapture experiments. The data for analysis consists of the frequencies of the observable capture histories over the t capture occasions of the experiment. A capture history is a vector of zeros and ones where one stands for a capture and zero for a miss. Rcapture can fit three types of models. With a closed population model, the goal of the analysis is to estimate the size N of the population which is assumed to be constant throughout the experiment. The estimator depends on the way in which the capture probabilities of the animals vary. Rcapture features several models for these capture probabilities that lead to different estimators for N. In an open population model, immigration and death occur between sampling periods. The estimation of survival rates is of primary interest. Rcapture can fit the basic Cormack-Jolly-Seber and Jolly-Seber model to such data. The third type of models fitted by Rcapture are robust design models. It features two levels of sampling; closed population models apply within primary periods and an open population model applies between periods. Most models in Rcapture have a loglinear form; they are fitted by carrying out a Poisson regression with the R function glm. Estimates of the demographic parameters of interest are derived from the loglinear parameter estimates; their variances are obtained by linearization. The novel feature of this package is the provision of several new options for modeling capture probabilities heterogeneity between animals in both closed population models and the primary periods of a robust design. It also implements many of the techniques developed by R. M. Cormack for open population models.

Rcapture: Loglinear Models for Capture-Recapture in R

This article introduces Rcapture, an R package for capture-recapture experiments. The data for analysis consists of the frequencies of the observable capture histories over the t capture occasions of the experiment. A capture history is a vector of zeros and ones where one stands for a capture and zero for a miss. Rcapture can fit three types of models. With a closed population model, the goal of the analysis is to estimate the size N of the population which is assumed to be constant throughout the experiment. The estimator depends on the way in which the capture probabilities of the animals vary. Rcapture features several models for these capture probabilities that lead to different estimators for N. In an open population model, immigration and death occur between sampling periods. The estimation of survival rates is of primary interest. Rcapture can fit the basic Cormack-Jolly-Seber and Jolly-Seber model to such data. The third type of models fitted by Rcapture are robust design models. It features two levels of sampling; closed population models apply within primary periods and an open population model applies between periods. Most models in Rcapture have a loglinear form; they are fitted by carrying out a Poisson regression with the R function glm. Estimates of the demographic parameters of interest are derived from the loglinear parameter estimates; their variances are obtained by linearization. The novel feature of this package is the provision of several new options for modeling capture probabilities heterogeneity between animals in both closed population models and the primary periods of a robust design. It also implements many of the techniques developed by R. M. Cormack for open population models

Sensei : enforcing secure coding guidelines in the integrated development environment

We discuss the potential benefits, requirements, and implementation challenges of a security-by-design approach in which an integrated development environment (IDE) plugin assists software developers to write code that complies with secure coding guidelines. We discuss how such a plugin can enable a company's policy-setting security experts and developers to pass their knowledge on to each other more efficiently, and to let developers more effectively put that knowledge into practice. This is achieved by letting the team members develop customized rule sets that formalize coding guidelines and by letting the plugin check the compliance of code being written to those rule sets in real time, similar to an as-you-type spell checker. Upon detected violations, the plugin suggests options to quickly fix them and offers additional information for the developer. We share our experience with proof-of-concept designs and implementations rolled out in multiple companies, and present some future research and development directions

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 Comprehensive Evaluation of Capture-Recapture Models for Estimating Software Defect Content

An important requirement to control the inspection of software artifacts is to be able to decide, based on more objective information, whether the inspection can stop or whether it should continue to achieve a suitable level of artifact quality. A prediction of the number of remaining defects in an inspected artifact can be used for decision making. Several studies in software engineering have considered capture-recapture models, originally proposed by biologists to estimate animal populations, to make a prediction. However, few studies compare the actual number of remaining defects to the one predicted by a capture-recapture model on real software engineering artifacts. Thus, there is little work looking at the robustness of capture-recapture models under realistic software engineering conditions, where it is expected that some of their assumptions will be violated. Simulations have been performed but no definite conclusions can be drawn regarding the degree of accuracy of such models under realistic inspection conditions, and the factors affecting this accuracy. Furthermore, the existing studies focused on a subset of the existing capture-recapture models. Thus a more exhaustive comparison is still missing. In this study, we focus on traditional inspections and estimate, based on actual inspections' data, the degree of accuracy of relevant, state-of-the-art capture-recapture models, as they have been proposed in biology and for which statistical estimators exist. In order to assess their robustness, we look at the impact of the number of inspectors and the number of actual defects on the estimators' accuracy based on actual inspection data. Our results show that models are strongly affected by the number of inspectors and, therefore, one must consider this factor befo..

A Comprehensive Evaluation of Capture-Recapture Models for Estimating Software Defect Content

An important requirement to control the inspection of software artifacts is to be able to decide, based on more objective information, whether the inspection can stop or whether it should continue to achieve a suitable level of artifact quality. A prediction of the number of remaining defects in an inspected artifact can be used for decision making. Several studies in software engineering have considered capture-recapture models, originally proposed by biologists to estimate animal populations, to make a prediction. However, few studies compare the actual number of remaining defects to the one predicted by a capture-recapture model on real software engineering artifacts. Thus, there is little work looking at the robustness of capture-recapture models under realistic software engineering conditions, where it is expected that some of their assumptions will be violated. Simulations have been performed but no definite conclusions can be drawn regarding the degree of accuracy of such models under realistic inspection conditions, and the factors affecting this accuracy. Furthermore, the existing studies focused on a subset of the existing capture-recapture models. Thus a more exhaustive comparison is still missing. In this study, we focus on traditional inspections and estimate, based on actual inspections' data, the degree of accuracy of relevant, state-of-the-art capture-recapture models, as they have been proposed in biology and for which statistical estimators exist. In order to assess their robustness, we look at the impact of the number of inspectors and the number of actual defects on the estimators’ accuracy based on actual inspection data. Our results sho