International Journal of Software Engineering and Knowledge Engineering

A typical software development team leaves behind a large amount of information. This information takes different forms, such as mail messages, software releases, version control logs, defect reports, etc. softChange is a tool that retrieves this information, analyses and enhances it by finding new relationships amongst it, and then allows users to navigate and visualize this information. The main objective of softChange it to help programmers, their management and software evolution researchers in understanding how a software product has evolved since its conception.

As software systems evolve over a long time, non-trivial and often unintended relationships among system classes arise, which cannot be easily perceived through source-code reading. As a result, the developers' understanding of continuously evolving, large, long-lived systems deteriorates steadily. A most interesting relationship is class co-evolution: because of implicit design dependencies clusters of classes change in "parallel" ways and recognizing such co-evolution is crucial in effectively extending and maintaining the system. In this paper, we propose a data-mining method for recovering "hidden" co-evolutions of system classes. This method relies on our UML-aware structural differencing algorithm, UMLDiff, which, given a sequence of UML class models of an object-oriented software system, produces a sequence of "change records" that describe the design-level changes over its life span. The change records are analyzed from the perspective of each individual system class to extract "class change profiles". Each phase of a class change profile is then discretized and classified into one of two general change types: function extension or refactoring. Finally, the Apriori association-rule mining algorithm is applied to the database of categorical class change profiles, to elicit co-evolution patterns among two or more classes, which may be as yet undocumented and unknown. The recovered knowledge facilitates the overall understanding of system evolution and the planning of future maintenance activities. We report on one real world case study evaluating our approach.

The performance of a classification model is invariably affected by the characteristics of the measurement data it is built upon. If the quality of the data is generally poor, then the classification model will demonstrate poor performance. The detection and removal of noisy instances will improve quality of the data, and consequently, the performance of the classification model. We investigate a noise handling technique that attempts to improve the quality of datasets for classification purposes by eliminating instances that are likely to be noise. Our approach uses twenty five different classification techniques to create an ensemble filter for eliminating likely noise. The basic assumption is that if a given majority of classifiers in the ensemble misclassify an instance, then it is likely to be a noisy instance. Using a relatively large number of base-level classifiers in the ensemble filter facilitates in achieving the desired level of noise removal conservativeness with several possible levels of filtering. It also provides a higher degree of confidence in the noise elimination procedure as the results are less likely to get influenced by (possibly) inappropriate learning bias of a few algorithms with twenty five base-level classifiers than with relatively smaller number of base-level classifiers. Empirical case studies of two high assurance software projects demonstrates the effectiveness of our noise elimination approach by the significant improvement achieved in classification accuracies at various levels of noise filtering.

Meta-programs are generic, incomplete, adaptable programs that are instantiated at construction time to meet specific requirements. Templates and generative techniques are examples of meta-programming techniques. Understanding of meta-programs is more difficult than understanding of concrete, executable programs. Static and dynamic analysis methods have been applied to ease understanding of programs — can similar methods be used for meta-programs? In our projects, we build meta-programs with a meta-programming technique called XVCL. Meta-programs in XVCL are organized into a hierarchy of meta-components from which the XVCL processor generates concrete, executable programs that meet specific requirements. We developed an automated system that analyzes XVCL meta-programs, and presents developers with information that helps them work with meta-programs more effectively. Our system conducts both static and dynamic analysis of a meta-program. An integral part of our solution is a query language, FQL in which we formulate questions about meta-program properties. An FQL query processor automatically answers a class of queries. The analysis method described in the paper is specific to XVCL. However, the principle of our approach can be applied to other meta-programming systems. We believe readers interested in meta-programming in general will find some of the lessons from our experiment interesting and useful.

A partition-based framework is presented for a formal study of classification problems. An information table is used as a knowledge representation, in which all basic notions are precisely defined by using a language known as the decision logic language. Solutions to, and solution space of, classification problems are formulated in terms of partitions. Algorithms for finding solutions are modelled as searching in a space of partitions under the refinement order relation. We focus on a particular type of solutions called conjunctively definable partitions. Two level-wise methods for decision tree construction are investigated, which are related to two different strategies: local optimization and global optimization. They are not in competition with, but are complementary to each other. Experimental results are reported to evaluate the two methods.

This paper presents an empirical study analysing the relationship between a set of metrics for Fourth–Generation Languages (4GL) programs and their maintainability. An analysis has been made using historical data of several industrial projects and three different approaches: the first one relates metrics and maintainability based on techniques of descriptive statistics, and the other two are based on Data Mining techniques. A discussion on the results obtained with the three techniques is also presented, as well as a set of equations and rules for predicting the maintenance effort in this kind of programs. Finally, we have done experiments about the prediction accuracy of these methods by using new unseen data, which were not used to build the knowledge model. The results were satisfactory as the application of each technique separately provides useful perspective for the manager in order to get a complementary insight from data.

Mobile agent paradigm is recognized as a new environment for distributed computing and provides many merits such as mobility, security, self-decision, and so on. However, its security problems should be resolved to increase its application to a variety of real domains. Especially, we must guarantee integrity of transferred mobile agents. Although many mobile agent systems were developed, the integrity issue remains a critical one. In this paper, we propose an integrity checking mechanism to do the aforementioned issue. The proposed mechanism is independent of specific security frameworks and can be added and used easily for various mobile agent platforms.