Complex network theory has been successfully introduced into the field of software engineering. Many works in the literature have built complex networks in software, usually called software networks, to represent software structure. Such software networks and their related graph algorithms have been proved effective in predicting software bugs. However, the software networks used were unweighted and undirected, neglecting the strength and direction of the couplings. Worse still, they ignored many important types of couplings between classes, such as local variable, instantiates, and access. All of these greatly affect the accuracy of the software network in representing the topological detail of software projects and ultimately affect the metrics derived from it. In this work, an improved effort-aware bug prediction approach named CoreBug is proposed. First, CoreBug uses a weighted directed class dependency network (WDCDN) to precisely describe classes and their couplings, including nine coupling types and their different coupling strengths and directions. Second, a generalized k-core decomposition is introduced to compute the coreness of each class in the WDCDN. Third, CoreBug combines the coreness of each class with its relative risk, as returned by the logistic regression, to quantify the risk of a given class being buggy. Empirical results on eighteen Java projects show that CoreBug is superior to the state-of-the-art approaches according to the average ranking of the Friedman test.

Even though the number of services is increasing, a single service can just complete simple tasks. In the face of complex tasks, we require composite multiple services to complete them. For the purpose of improving the efficiency of web service composition, we propose a service composition approach based on an improved fireworks algorithm (FWA++). First, we use the strategy of random selection to keep individuals for the next generation, and the purpose is to speed up the convergence speed of the FWA++ and enhance the search ability. Second, we randomize the total number of sparks and maximum amplitude of sparks for each generation. In this way, the search ability and the ability to jump out of the local optimal solution are dynamically balanced throughout the execution of the algorithm. Our experimental results show that compared with other existing approaches, the approach proposed in this paper is more efficient and stable.

Mashup is a new type of application that integrates multiple Web APIs. For mashup application development, the quality of the selected APIs is particularly important. However, with the rapid development of Internet technology, the number of Web APIs is increasing rapidly. It is unrealistic for mashup developers to manually select appropriate APIs from a large number of services. For existing methods, there is a problem of data sparsity, because one mashup is related to a few APIs, and another problem of over-reliance on semantic information. To solve these problems in current service discovery approaches, we propose a service discovery approach based on a knowledge map (SDKG). We embed service-related information into the knowledge graph, alleviating the impact of data sparsity and mining deep relationships between services, which improves the accuracy of service discovery. Experimental results show that our approach has obvious advantages in accuracy compared with the existing mainstream service discovery approaches.

Technical debt prediction (TDP) is crucial for the long-term maintainability of software. In the literature, many machine-learning based TDP models have been proposed; they used TD-related metrics as input features for machine-learning classifiers to build TDP models. However, their performance is unsatisfactory. Developing and utilizing more effective metrics to build TDP models is considered as a promising approach to enhance the performance of TDP models. Social Network Analysis (SNA) uses a set of metrics (i.e., SNA metrics) to characterize software elements (classes, binaries, etc.) in software from the perspective of software as a whole. SNA metrics are regarded as a compensation of TD-related metrics used in the existing TDP work, and thus are expected to improve the performance of existing TDP models. However, the effectiveness of SNA metrics in the field of TDP has never been explored so far. To fill this gap, in this paper, we propose an improved software technical debt prediction approach. First, we represent software as a Class Dependency Network, based on which we compute the value of a set of SNA metrics. Second, we combine SNA metrics with the TD-related metrics to create a combined metric suite (CMS). Third, we employ CMS as the input features and utilize seven commonly used machine learning classifiers to build TDP models. Empirical results on a publicly available data set show that (i) the combined metric suite (i.e., CMS) can indeed improve the performance of existing TDP models; (ii) XGBoost performs best among the seven classifiers, with an F2 value of 0.77, an MI ratio of approximately 0.10, and a recall close to 0.87. Furthermore, we also reveal the relative effectiveness of different metric combinations.

Key classes are the most important classes in a software system, which provide an excellent foundation for developers—especially those new to the field—to understand unfamiliar software systems. In the past decade, several key class prediction (KCP) approaches have been proposed. They used design metrics extracted from source code and unweighted network metrics computed on class coupling networks as features and built machine-learning models to predict whether a class is a key class or not. However, previous studies mainly focused on improving the performance of KCP models in the within-project (i.e., KCP in the same project) context, and the network metrics they used are unweighted and inaccurate, as they are computed on unweighted and incomplete class coupling networks. These limitations lead to a lack of thorough evaluation of the effectiveness of network metrics for KCP, especially in the cross-project (KCP across diverse projects) context, which in turn results in uncertainty about how to choose suitable metrics as features when building KCP models. To fill this gap, in this paper, we thoroughly evaluate the effectiveness of network metrics for KCP. Specifically, we build weighted and more complete class coupling networks for software, and introduce a set of weighted network metrics to characterize class complexity. Then, we build different KCP models using the Random Forest learner and the Naive Bayes model for two KCP contexts (i.e., within-project and cross-project), respectively, with design metrics, unweighted/weighted network metrics, and their combinations being features. Finally, through an empirical study on 18 open-source Java projects, we thoroughly investigate the relative effectiveness of network metrics over design metrics across the two KCP contexts. Our results suggest that when building KCP models, to achieve better performance, researchers and practitioners should consider using unweighted (or weighted) network metrics alone or along with design metrics in the within-project KCP context, using design metrics alone or along with unweighted (or weighted) network metrics in the cross-project KCP context, and using unweighted (or weighted) network metrics along with design metrics across the two KCP contexts.

This paper discusses using 3D model search techniques to build and access case study libraries that act as an interdisciplinary tool for engineering education. After introducing the concept and benefits of managing case study libraries by 3D model search techniques, we present a prototype system of a case study library for mechanical parts development. Several aspects of the prototype system are discussed, including user interface, shape comparison, case browsing and user feedback. The paper ends with some examples of cases retrieved from our library.