International Journal of Software Engineering and Knowledge Engineering

The runtime environment and workload of software are constantly changing, requiring users to make appropriate adjustments to accommodate these changes. The runtime configuration, however, as the interface for users to manipulate software behavior often requires domain-specific knowledge to understand. This usually results in users spending a considerable amount of time wading through document and user manuals trying to understand the runtime configuration. In this paper, we study the possibility of understanding the intention of runtime configuration options through their documents, even sometimes it is difficult for users to understand. Based on these studies, we classify the runtime configuration option’s intention into six categories. Accordingly, we design runtime Configuration Intention Classifier (CIC), a supervised approach based on CNN to classify the runtime configuration option’s intention according to its document. CIC integrates the features of runtime configuration names and descriptions according to different levels of granularity and predicts the intention of runtime configuration options accordingly. Extensive experiments show that our approach can achieve an accuracy of 85.6% and outperform nine comparative approaches by up to 16.6% over the dataset we customized.

Bad smells represent imperfection in the design of the software system and trigger the urge to refactor the source code. The quality of object-oriented software has always been a major concern for the developer team and refactoring techniques help them to focus on this aspect by transforming the code in a way such that the behavior of the software can be preserved. Rigorous research has been done in this field to improve the quality of the software using various techniques. But, one of the issues still remains unsettled, i.e. the overhead effort to refactor the code in order to yield the maximum maintainability value. In this paper, a quantitative evaluation method has been proposed to improve the maintainability value by identifying the most optimum refactoring dependencies in advance with the help of various meta-heuristic algorithms, including A^*, AO^*, Hill-Climbing and Greedy approaches. A comparison has been done between the maintainability values of the software used, before and after applying the proposed methodology. The results of this study show that the Greedy algorithm is the most promising algorithm amongst all the algorithms in determining the most optimum refactoring sequence resulting in 18.56% and 9.90% improvements in the maintainability values of jTDS and ArtOfIllusion projects, respectively. Further, this study would be beneficial for the software maintenance team as refactoring sequences will be available beforehand, thereby helping the team in maintaining the software with much ease to enhance the maintainability of the software. The proposed methodology will help the maintenance team to focus on a limited portion of the software due to prioritization of the classes, in turn helping them in completing their work within the budget and time constraints.

Code clone serves as a typical programming manner that reuses the existing code to solve similar programming problems, which greatly facilitates software development but recurs program bugs and maintenance costs. Recently, deep learning-based detection approaches gradually present their effectiveness on feature representation and detection performance. Among them, deep learning approaches based on abstract syntax tree (AST) construct models relying on the node embedding technique. In AST, the semantic of nodes is obviously hierarchical, and the importance of nodes is quite different to determine whether the two code fragments are cloned or not. However, some approaches do not fully consider the hierarchical structure information of source code. Some approaches ignore the different importance of nodes when generating the features of source code. Thirdly, when the tree is very large and deep, many approaches are vulnerable to the gradient vanishing problem during training. In order to properly address these challenges, we propose a hierarchical attentive graph neural network embedding model-HAG for the code clone detection. Firstly, the attention mechanism is applied on nodes in AST to distinguish the importance of different nodes during the model training. In addition, the HAG adopts graph convolutional network (GCN) to propagate the code message on AST graph and then exploits a hierarchical differential pooling GCN to sufficiently capture the code semantics at different structure level.

To evaluate the effectiveness of HAG, we conducted extensive experiments on public clone dataset and compared it with seven state-of-the-art clone detection models. The experimental results demonstrate that the HAG achieves superior detection performance compared with baseline models. Especially, in the detection of moderately Type-3 or Type-4 clones, the HAG particularly outperforms baselines, indicating the strong detection capability of HAG for semantic clones. Apart from that, the impacts of the hierarchical pooling, attention mechanism and critical model parameters are systematically discussed.

Together with the spread of DevOps practices and container technologies, Microservice Architecture has become a mainstream architecture style in recent years. Resilience is a key characteristic in Microservice Architecture (MSA) Systems, and it shows the ability to cope with various kinds of system disturbances which cause degradations of services. However, due to lack of consensus definition of resilience in the software field, although a lot of work has been done on resilience for MSA Systems, developers still do not have a clear idea on how resilient an MSA System should be, and what resilience mechanisms are needed.

In this paper, by referring to existing systematic studies on resilience in other scientific areas, the definition of microservice resilience is provided and a Microservice Resilience Measurement Model is proposed to measure service resilience. And a requirement model to represent resilience requirements of MSA Systems is given. The requirement model uses elements in KAOS to represent notions in the measurement model, and decompose service resilience goals into system behaviors that can be executed by system components. As a proof of concept, a case study is conducted on an MSA System to illustrate how the proposed models are applied.

The heterogeneous defect prediction (HDP) technique can predict defects in a target company using heterogeneous metric data from external company, which has received substantial research attention. However, existing HDP methods assume that source data is labeled but labeling data is expensive. Semi-supervised defect prediction technique can perform defect prediction with few labeled data. In this paper, we investigate a new problem — semi-supervised HDP (SHDP). To solve this problem, we propose a new approach named cost-sensitive kernel semi-supervised correlation analysis (CKSCA) as a solution of SHDP problem. It introduces unified metric representation and canonical correlation analysis to make the data distributions of different company projects more similar. CKSCA also designs a cost-sensitive kernel semi-supervised discriminant analysis mechanism to utilize the limited labeled data and sufficient real-life unlabeled data from different companies. Besides we collect lots of open-source projects from GitHub website to construct a new large-scale unlabeled dataset called GITHUB dataset. It contains 26,407 modules and is greater than each public project dataset. It has been public online and can be extended continuously. Experiments on the GITHUB dataset and other public datasets indicate that unlabeled GITHUB data can help prediction model improve prediction performance, and CKSCA is effective and efficient for solving SHDP problem.