The large number of new bug reports received in bug repositories of software systems makes their management a challenging task. Handling these reports manually is time consuming, and often results in delaying the resolution of important bugs. To address this issue, a recommender may be developed which automatically prioritizes the new bug reports. In this paper, we propose and evaluate a classification based approach to build such a recommender. We use the Naïve Bayes and Support Vector Machine (SVM) classifiers, and present a comparison to evaluate which classifier performs better in terms of accuracy. Since a bug report contains both categorical and text features, another evaluation we perform is to determine the combination of features that better determines the priority of a bug. To evaluate the bug priority recommender, we use precision and recall measures and also propose two new measures, Nearest False Negatives (NFN) and Nearest False Positives (NFP), which provide insight into the results produced by precision and recall. Our findings are that the results of SVM are better than the Naïve Bayes algorithm for text features, whereas for categorical features, Naïve Bayes performance is better than SVM. The highest accuracy is achieved with SVM when categorical and text features are combined for training.

Cloning in software represents similar program structures having its own benefits and drawbacks. Proper clone analysis is required to exploit the benefits of clones. A study of software clone evolution serves the purpose of understanding the maintenance implications of clones, which leads to their appropriate management. Structural clones (recurring patterns of simple clones) represent design level similarities in software. Evolutionary characteristics of clones can assess the relevance of those clones for software developers and maintainers. Although the evolution of simple clones has been thoroughly studied by researchers, the evolution of structural clones is still to be explored. In this paper, we study the evolution of structural clones by performing a longitudinal study on multiple versions of five Java systems. To perform a systematic study of the structural clone evolution, we define structural clones and their evolution patterns in a formal notation. Our results show that structural clones are more likely to change inconsistently, however, less frequently than simple clones, whereas the lifetime of the structural clones is similar to that of the simple clones. Evolutionary characteristics of structural clones suggest that they require more attention in their management. Analysis of structural clone evolution reveals similar reasons for changes, and similar trends in evolution patterns, for all subject systems. These trends reveal evolutionary characteristics of structural clones that can help in devising appropriate strategies for managing them, hence devising better clone management systems.INDEX TERMS Clone evolution, software clones, structural clones, software engineering, software evolution, software design.JAWERIA KANWAL received the M.Phil. degree in computer science from Quaid-i-Azam University, Islamabad, Pakistan, in 2011, where she is currently pursuing the Ph.D. degree in computer science.

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Maintenance of architectural documentation is a prime requirement for evolving software systems. New versions of software systems are launched after making the changes that take place in a software system over time. The orphan adoption problem, which deals with the issue of accommodation of newly introduced resources (orphan resources) in appropriate subsystems in successive versions of a software system, is a significant problem. The orphan adoption algorithm has been developed to address this problem. For evolving software systems, it would be useful to recover the architecture of subsequent versions of a software system by using existing architectural information. In this paper, we explore supervised learning techniques (classifiers) for recovering the architecture of subsequent versions of a software system by taking benefit of existing architectural information. We use three classifiers, i.e., Bayesian classifier, k-Nearest Neighbor classifier and Neural Network for orphan adoption. We conduct experiments to compare the performance of the classifiers using various dependencies between entities in a software system. Our experiments highlight correspondence between the orphan adoption algorithm and the classifiers, and also reveal their strengths and weaknesses. To combine strengths of individual classifiers, we propose using a multiclassifier approach in which classifiers work cooperatively to improve classification accuracy. Experiments show that there is significant improvement in results when our proposed multiclassifier approach is used.

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