International Journal of Software Engineering and Knowledge Engineering

Feature engineering is one aspect of knowledge engineering. Besides feature selection, the appropriate assignment of feature values is also crucial to the performance of many software applications, such as text categorization (TC) and speech recognition. In this work, we develop a general method to enhance TC performance by the use of context-dependent feature values (aka term weights), which are obtained by a novel adaptation of a context-dependent adjustment procedure previously shown to be effective in information retrieval. The motivation of our approach is that the general method can be used with different text representations and in combination of other TC techniques. Experiments on several test collections show that our context-dependent feature values can improve TC over traditional context-independent unigram feature values, using a strong classifier like Support Vector Machine (SVM), which past works have found to be hard to improve. We also show that the relative performance improvement of our method over the context-independent baseline is comparable to the levels attained by recent word embedding methods in the literature, while an advantage of our approach is that it does not require the substantial training needed to learn word embedding representations.

There is a growing interest in leveraging Deep Learning (DL) for automating Software Engineering tasks such as program completion. In this paper, we leverage Recurrent Neural Networks (RNNs) for Abstract Syntax Tree (AST)-based code completion. Our approach converts source code into AST nodes and a language model predicts the type and value attributes of next tokens. Our work demonstrates that the attention augmented RNN-based language models are able to understand local context and copy from recent past tokens which have never appeared in the training data set. We observed a drop of performances of both type and value predictions when using a traditional pointer network architecture for out-of-vocabulary (OoV) copying and context understanding, which we call multi-task conflict. To address this challenge, we have devised a new structure of self-attention called Split Attention, where two separate dot-product layers are applied to different parts of the history cache. Based on this structure, we propose a new network called Split Attention Pointer Network (SAPN), which is efficient and flexible in both learning local context and copying OoV tokens from history. The empirical results suggest that our model is superior in syntax-aware generation and OoV token prediction by demonstrating attention behavior similar to human programmers. The results also indicate that our model out performs previous state-of-the-art approaches by more than 6% on widely recognized program completion benchmarks.

Software testing is a continuous process during the software development stages to ensure quality software products. Researchers, experts and software engineers keep going on studying new techniques, methods and approaches of testing to accommodate changes in software development because of the flexible requirement along with the changing of technology. So, developers and testers need to have effective methods, tools and approaches to create a high-quality product at an efficient cost. This paper provides an effective approach for context-driven testing (CDT) in an agile software development process. CDT is a testing approach that supports the tester to choose their testing techniques and test objectives based on specific contexts. The aim of this paper is to propose an effective approach for implementing the CDT in practice, called CDTiP. Through an analysis of two case studies using an agile development process with different contexts, we validate the effectiveness of the approach in terms of test coverage, detect errors, test effort. The empirical results show that CDTiP is suitable for the agile development process that can help the tester to detect defects faster at minimum cost. The results of this method have been applied at Enclave, an ODC Software Engineering company, on real projects.

Programming in pictures is an approach where pictures and moving pictures are used as super-characters to represent the features of computational algorithms and data structures, as well as for explaining the models and application methods involved. *AIDA is a computer language that supports programming in pictures. This language and its environment have been developed and promoted as a testbed for various innovations in information technology (IT) research and implementation, including exploring the compactness of the programs and their adaptive software systems, and obtaining better understanding of information resources. In this paper, new features of the environment and methods of their implementation are presented. They are considered within a case study of a large-scale module of a nuclear safety analysis system to demonstrate that *AIDA language is appropriate for developing efficient codes of serious applications and for providing support, based on folding/unfolding techniques, enhancing the readability, maintainability and algorithmic transparency of programs. Features of this support and the code efficiency are presented through the results of a computational comparison with a FORTRAN equivalent.

Move Method Refactoring (MMR) is used to place highly coupled methods in appropriate classes for making source code more cohesive. Like other refactoring techniques, it is mandatory that applying MMR will preserve applications’ behaviors. However, traditional MMR techniques failed to meet this essential precondition for Action methods in web-based application and API methods in libraries projects. The reason is that applying MMR on these methods changes the behaviors of the projects by raising Application-breaking issues, for instance, failure of browser requests and compilation errors in client projects. To resolve this problem, developers are suggested to manually check Action and API methods while applying MMR. However, manually inspecting thousands of lines of code for these issues is a time-consuming and hectic task. In this paper, an advanced MMR technique is proposed which automatically identifies Application-breaking MMR suggestions. This technique first takes the initial move method suggestions from the existing prominent MMR techniques e.g. JDeodorant. For each of the suggestions, it parses the source code and construct Abstract Syntax Tree to examine two types of usage. One is whether a suggestion has not been used in any unit test and Regular Class, and another is whether the suggestion has been used in unit test classes only. If any MMR suggestion is found having one of these two types of usage or both, the respective suggestion is marked as Application-breaking. In order to evaluate the proposed technique, several experiments have been conducted on open source projects. The experimental results show that the proposed technique achieved 96.4% Precision, 90% Recall and 93.1% F-score in detecting Application-breaking MMR suggestions, because of considering external dependencies of the MMR suggestions.

The color design is one of the important parts of GUI development. To gain an attractive color scheme, designers often seek inspiration from examples. However, transferring an example’s colors to a target web page is time-consuming and tedious. In this paper, we propose a method named AutoWPR to reuse the example web page’s colors for recoloring a web page. To preserve the semantic relations of web elements, we propose a clustering algorithm to group the related elements into a cluster. In order to make the recoloring result have similar color distributions to the example, we use the Random–Forest regression to learn human’s mappings and propose a top-down matching algorithm to generate a mapping between two web pages’ clusters. Then AutoWPR recolors the element with the matching element’s colors. We designed several experiments to evaluate the correctness of the clustering and matching algorithm. We also conducted some qualitative and quantitative experiments to evaluate the effectiveness of our results in helping recoloring. The results show that our method can generate a human-like recoloring result and help novice developers reuse the reference web page’s colors conveniently.

Multi-agent systems and agent-oriented methodologies support analysis, characterization and development of complex software systems. These methodologies introduce different definitions for the essential components of multi-agent systems and cover different phases of the system development life cycle. Therefore, appropriate frameworks for evaluation and comparison of different methodologies would support developers to adopt the best methodology, or a combination of different methodologies, based on the project requirements. This review covers the system development phases and the main conceptual components in the context of multi-agent systems. Then, the evaluation frameworks proposed in the literature for comparison and evaluation of agent-oriented methodologies are reviewed. Evaluation frameworks proposed in the literature are categorized into three categories: methodology-based, phase-based and feature-based evaluation frameworks. The paper concludes with the agent-oriented methodologies’ usage challenges, their current limitations and potential future directions.