Many organizations want to predict the number of defects (faults) in software systems, before they are deployed, to gauge the likely delivered quality and maintenance effort. To help in this numerous software metrics and statistical models have been developed, with a correspondingly large literature. We provide a critical review of this literature and the state-of-the-art. Most of the wide range of prediction models use size and complexity metrics to predict defects. Others are based on testing data, the “quality ” of the development process, or take a multivariate approach. The authors of the models have often made heroic contributions to a subject otherwise bereft of empirical studies. However, there are a number of serious theoretical and practical problems in many studies. The models are weak because of their inability to cope with the, as yet, unknown relationship between defects and failures. There are fundamental statistical and data quality problems that undermine model validity. More significantly many prediction models tend to model only part of the underlying problem and seriously misspecify it. To illustrate these points the “Goldilock’s Conjecture,” that there is an optimum module size, is used to show the considerable problems inherent in current defect prediction approaches. Careful and considered analysis of past and new results shows that the conjecture lacks support and that some models are misleading. We recommend holistic models for software defect prediction, using Bayesian Belief Networks, as alternative approaches to the single-issue models used at present. We also argue for research into a theory of “software decomposition” in order to test hypotheses about defect introduction and help construct a better science of software engineering.

Abstract not found

Design patterns offer flexible solutions to common problems in software development. Recent studies have shown that several design patterns involve crosscutting concerns. Unfortunately, object-oriented (OO) abstractions are often not able to modularize those crosscutting concerns, which in turn decrease the system reusability and maintainability. Hence, it is important verifying whether aspect-oriented approaches support improved modularization of crosscutting concerns relative to design patterns. Ideally, quantitative studies should be performed to compare OO and aspect-oriented implementations of classical patterns with respect to important software engineering attributes, such as coupling and cohesion. This paper presents a quantitative study that compares aspect-based and OO solutions for the 23 Gang-of-Four patterns. We have used stringent software engineering attributes as the assessment criteria. We have found that most aspect-oriented solutions improve separation of patternrelated concerns, although only 4 aspect-oriented implementations have exhibited significant reuse.

The reengineering and reverse engineering of software systems is gaining importance in software industry, because the accelerated turnover in software companies creates legacy systems in a shorter period of time. Especially understanding classes is a key activity in object-oriented programming, since classes represent the primary abstractions from which applications are built. The main problem of this task is to quickly grasp the purpose of a class and its inner structure. To help the reverse engineers in their first contact with a foreign system, we propose a categorization of classes based on the visualization of their internal structure. The contributions of this paper are a novel categorization of classes and a visualization of the classes which we call the class blueprint. We have validated the categorization on several case studies, two of which we present here. Keywords Reverse Engineering, Program Understanding, Software Visualization, Visual Patterns, Smalltalk 1.

Reverse engineering software systems has become a major concern in software industry because of their sheer size and complexity. This problem needs to be tackled, since the systems in question are of considerable worth to their owners and maintainers. In this article we present the concept of a polymetric view, a lightweight software visualization technique enriched with software metrics information. Polymetric views help to understand the structure and detect problems of a software system in the initial phases of a reverse engineering process. We discuss the benefits and limits of several predefined polymetric views we have implemented in our tool CodeCrawler. Moreover, based on clusters of different polymetric views we have developed a methodology which supports and guides a software engineer in the first phases of a reverse engineering of a large software system. We have refined this methodology by repeatedly applying it on industrial systems, and illustrate it by applying a selection of polymetric views to a case study.

In order to support the maintenance of an objectoriented software system, the quality of its design must be evaluated using adequate quantification means. In spite of the current extensive use of metrics, if used in isolation metrics are oftentimes too fine grained to quantify comprehensively an investigated design aspect (e.g., distribution of system’s intelligence among classes). To help developers and maintainers detect and localize design problems in a system, we propose a novel mechanism – called detection strategy – for formulating metrics-based rules that capture deviations from good design principles and heuristics. Using detection strategies an engineer can directly localize classes or methods affected by a particular design flaw (e.g., God Class), rather than having to infer the real design problem from a large set of abnormal metric values. We have defined such detection strategies for capturing around ten important flaws of object-oriented design found in the literature and validated the approach experimentally on multiple large-scale case-studies.

One of the most important steps towards e€ective software maintenance of a large complicated system is to understand how program features are spread over the entire system and their interactions with the program components. However, we must ®rst be able to represent an abstract feature in terms of some concrete program components. In this paper, we use an execution slice-based technique to identify the basic blocks which are used to implement a program feature. Three metrics are then de®ned, based on this identi®cation, to determine quantitatively, the disparity between a program component and a feature, the concentration of a feature in a program component, and the dedication of a program component to a feature. The computations of these metrics are automated by incorporating them in a tool (vSuds), which makes the use of our metrics immediately applicable in real-life contexts. We demonstrate the e€ectiveness of our technique by experimenting with a reliability and performance evaluator. Results of our study suggest that these metrics can provide an indication of the closeness between a feature and a program component which is very

Abstract—The value of using static code attributes to learn defect predictors has been widely debated. Prior work has explored issues like the merits of “McCabes versus Halstead versus lines of code counts ” for generating defect predictors. We show here that such debates are irrelevant since how the attributes are used to build predictors is much more important than which particular attributes are used. Also, contrary to prior pessimism, we show that such defect predictors are demonstrably useful and, on the data studied here, yield predictors with a mean probability of detection of 71 percent and mean false alarms rates of 25 percent. These predictors would be useful for prioritizing a resource-bound exploration of code that has yet to be inspected. Index Terms—Data mining detect prediction, McCabe, Halstead, artifical intelligence, empirical, naive Bayes. 1

Test-driven development is a software development practice that has been used sporadically for decades. With this practice, test cases (preferably automated) are incrementally written before production code is implemented. Test-driven development has recently reemerged as a critical enabling practice of the Extreme Programming software development methodology. We ran a case study of this practice at IBM. In the process, a thorough suite of automated test cases was produced. In this case study, we found that the code developed using a test-driven development practice showed, during functional verification and regression tests, approximately 40 % fewer defects than a baseline prior product developed in a more traditional fashion. The productivity of the team was not impacted by the additional focus on producing automated test cases. This test suite will aid in future enhancements and maintenance of this code. The experiment and the results are discussed in detail. 1.

this article, please send e-mail to: tse@computer.org, and reference IEEECS Log Number 113955