The increasing complexity and shorter life cycle of software have made it necessary to reuse software. Object-oriented development has not facilitated extensive reuse of software and it has become difficult to manage and understand modern object-oriented systems which have become very extensive and complex. However, components, compared with objects, provide more advanced means of structuring, describing and developing systems, because they are more coarse grained and have more domain-specific aspects than objects. In addition, they are also suited to a current distributed environment due to their reusability, maintainability and granularity. In this paper, we present a process of extracting components from object-oriented systems. We define some static metrics and guidelines that can be applied to transform object-oriented systems into component-based systems. Our process consists of two parts. First, basic components are created based on composition and inheritance relationships between classes. Second, the intermediate system is refined into a component-based system with our proposed static metrics and guidelines.

Many software development teams usually tend to focus on maintenance activities in general. Recently, many studies on bug severity prediction have been proposed to help a bug reporter determine severity. But they do not consider the reporter's expression of emotion appearing in the bug report when they predict the bug severity level. In this paper, we propose a novel approach to severity prediction for reported bugs by using emotion similarity. First, we do not only compute an emotion-word probability vector by using smoothed unigram model (UM), but we also use the new bug report to find similar-emotion bug reports with Kullback-Leibler divergence (KL-divergence). Then, we introduce a new algorithm, Emotion Similarity (ES)-Multinomial, which modifies the original Naïve Bayes Multinomial algorithm. We train the model with emotion bug reports by using ES-Multinomial. Finally, we can predict the bug severity level in the new bug report. To compare the performance in bug severity prediction, we select related studies including Emotion Words-based Dictionary (EWD)-Multinomial, Naïve Bayes Multinomial, and another study as baseline approaches in open source projects (e.g., Eclipse, GNU, JBoss, Mozilla, and WireShark). The results show that our approach outperforms the baselines, and can reflect reporters' emotional expressions during the bug reporting.

Software with design flaws increases maintenance costs, decreases component reuse, and reduces software life. Even well-designed software tends to deteriorate with time as it undergoes maintenance. Work on restructuring object-oriented designs involves estimating the quality of the designs using metrics, and automating transformations that preserve the behavior of the designs. However, these factors have been treated almost independently of each other. A long-term goal is to define transformations preserving the behavior of object-oriented designs, and automate the transformations using metrics. In this paper, we describe a genetic algorithm based restructuring approach using metrics to automatically modify object-oriented designs. Cohesion and coupling metrics based on abstract models are defined to quantify designs and provide criteria for comparing alternative designs. The abstract models include a call-use graph and a class-association graph that represent methods, attributes, classes, and their relationships. The metrics include cohesion, inheritance coupling, and interaction coupling based on the behavioral similarity between methods extracted from the models. We define restructuring operations, and show that the operations preserve the behavior of object-oriented designs. We also devise a fitness function using cohesion and coupling metrics, and automatically restructure object-oriented designs by applying a genetic algorithm using the fitness function.

Software products are increasingly complex, so it is becoming more difficult to find and correct bugs in large programs. Software developers rely on bug reports to fix bugs; thus, bug-tracking tools have been introduced to allow developers to upload, manage, and comment on bug reports to guide corrective software maintenance. However, the very high frequency of duplicate bug reports means that the triagers who help software developers in eliminating bugs must allocate large amounts of time and effort to the identification and analysis of these bug reports. In addition, classifying bug reports can help triagers arrange bugs in categories for the fixers who have more experience for resolving historical bugs in the same category. Unfortunately, due to a large number of submitted bug reports every day, the manual classification for these bug reports increases the triagers' workload. To resolve these problems, in this study, we develop a novel technique for automatic duplicate detection and classification of bug reports, which reduces the time and effort consumed by triagers for bug fixing. Our novel technique uses a support vector machine to check whether a new bug report is a duplicate. The concept profile is also used to classify the bug reports into related categories in a taxonomic tree. Finally, we conduct experiments that demonstrate the feasibility of our proposed approach using bug reports extracted from the large-scale open source project Mozilla.