International Journal of Software Engineering and Knowledge Engineering

Obfuscation is a technology that secures software artifacts from reverse engineering by making its cost prohibitively high. Intermediate level obfuscator implements the defensive mechanisms inside the software, and owing to high potency and resilience, can successfully secure the sensitive software components. This paper provides an analysis and parametrization of the obfuscator, as well as a method of fine-tuning and evaluating obfuscating transformations in terms of potency, resilience and cost.

Heterogeneous networks are ubiquitous. People like to discover rare but meaningful objects and patterns from such networks. Regardless of high structure similarity or high content similarity, the corresponding objects can be used in data analysis. However, the vast differences between structure and contents should be paid more attention. In this paper, we propose an outlier correlation detection method, called Structure2Content, which discovers outlier correlation incrementally in structure-level and content-level. Structure2Content addresses three important challenges: (1) how can we measure the target object’s structure and content similarity? (2) how can we find the representative features of target objects? (3) how can we insert new data or delete the obsoleted data incrementally. To tackle these challenges, Structure2Content applies four main techniques: (1) two matrices are used to store structure and content similarity, respectively, (2) 3-tuples are used to represent the closeness degree between objects, (3) a mirror step and an iterative process are combined to obtain the top-K outlier correlations, and (4) only updating 3-tuples can help insert or delete data incrementally instead of training all data from the beginning. Substantial experiments show that our proposed method is very effective for outlier correlations detection.

Software remodularization seeks to cluster software modules with high cohesion and low coupling: such a structure can help the comprehension and maintenance of complex systems. The modularization quality is usually captured using either structural, semantic, or history-based factors. All existing techniques apply a single factor to the entire system, which raises the following issues. First, a single factor may fail to capture the quality across the entire project: some modules may form semantic bondings, while others may form more structural ones. Second, the user of the technique has to choose a factor without knowing which one would perform the best. To resolve these issues, we propose a multi-factor module clustering, in which module clusters can be formed based on different factors. Our technique not only allows module clusters of different natures, but also relieve users from having to select a single factor. The paper introduces two different search-based formulations of multi-factor remodularization, and compares these against single-factor remodularization using four heterogeneous factors and six open source projects. The evaluation results show that the multi-factor remodularization can produce solutions that are 10.69% closer to the actual modularization adopted by the developers as compared with those produced by single-factor remodularization on average.

We present a study of 600 Java software networks with the aim of characterizing the relationship among their defectiveness and community metrics. We analyze the community structure of such networks, defined as their topological division into subnetworks of densely connected nodes. A high density of connections represents a higher level of cooperation between classes, so a well-defined division in communities could indicate that the software system has been designed in a modular fashion and all its functionalities are well separated. We show how the community structure can be an indicator of well-written, high quality code by retrieving the communities of the analyzed systems and by ranking their division in communities through the built-in metric called modularity. We found that the software systems with highest modularity possess the majority of bugs, and tested whether this result is related to some confounding effect. We found two power laws relating the maximum defect density with two different metrics: the number of detected communities inside a software network and the clustering coefficient. We finally found a linear correlation between clustering coefficient and number of communities. Our results can be used to make predictive hypotheses about software defectiveness of future releases of the analyzed systems.

To better understand and exploit the knowledge necessary to comprehend and evolve an existing system, different models can be extracted from it. Models represent the extracted information at various abstraction levels, and are useful to document, maintain, and reengineer the system. The Knowledge Discovery Metamodel (KDM) has been defined by the object management group as a meta-model supporting a large share of reverse engineering activities. Its specification has also been adopted by the ISO in 2012. This paper explores and describes alternative meta-models proposed in the literature to support reverse engineering, program comprehension, and software evolution activities. We focus on the similarity and differences of the alternative meta-models with KDM, trying to understand the potentials of reciprocal information interchange. We describe KDM and other five meta-models, plus their extensions available in the literature and their diffusion in the reverse engineering community. We also investigate the approaches using KDM and the five meta-models. In the paper, we underline the limited reuse of models for reverse engineering, and identify potential directions for future related research, to enhance the existing models and ease the exchange of information among them.

Software repositories consist of thousands of applications and the manual categorization of these applications into domain categories is very expensive and time-consuming. In this study, we investigate the use of an ensemble of classifiers approach to solve the automatic software categorization problem when the source code is not available. Therefore, we used three data sets (package level/class level/method level) that belong to 745 closed-source Java applications from the Sharejar repository. We applied the Vote algorithm, AdaBoost, and Bagging ensemble methods and the base classifiers were Support Vector Machines, Naive Bayes, J48, IBk, and Random Forests. The best performance was achieved when the Vote algorithm was used. The base classifiers of the Vote algorithm were AdaBoost with J48, AdaBoost with Random Forest, and Random Forest algorithms. We showed that the Vote approach with method attributes provides the best performance for automatic software categorization; these results demonstrate that the proposed approach can effectively categorize applications into domain categories in the absence of source code.