International Journal of Software Engineering and Knowledge Engineering

Users frequently raise feedback when using software products. Feedback from users regarding their experiences and expectations and software defects they found adds values to software maintenance and evolution — software managers collect user feedback and then dispatch feedback issues that developers (and/or maintainers) need to track and process. Feedback tracking is often supported by open source platforms and collaborative software systems. Meanwhile, there still exists a gap between feedback issues and source code: since user feedback is usually informal and arbitrary, engineers have to spend much effort on comprehending issues and identifying which source code files need to be improved or fixed.

This paper introduces a deep learning approach, Feedback2Code, which facilitates identification of user-feedback-related source code files. The core idea is to (1) explore latent semantics of user feedback and source code using several deep learning techniques such as Multi-Layer Perceptron (MLP), Convolutional Neutral Network (CNN) and skip-gram and (2) establish a multi-correlation model to explore linkages between feedback issues and source code files. Given a feedback issue, the linkages then allow engineers to identify source code files that are highly relevant to the issue.

We have implemented Feedback2Code and evaluated it against ChangeAdvisor (a state-of-the-art approach) on 24 open source projects. The evaluation results clearly show the strength of Feedback2Code: for 103793 feedback issues, Feedback2Code successfully established 101190 feedback-code linkages and achieved a precision that is $3.11\times$ higher than that of ChangeAdvisor. Feedback2Code also achieved an MRR and an MAP that are $2.83\times$ and $2.79\times$ higher than those of ChangeAdvisor, respectively. Furthermore, we also found that a Feedback2Code-trained model can be easily transferred, allowing feedback-code linkages to be established in new projects with a little history data.

Software cost estimation is one of the most crucial tasks in a software development life cycle. Some well-proven methods and techniques have been developed for effort estimation in case of classical software. Mobile applications (apps) are different from conventional software by their nature, size and operational environment; therefore, the established estimation models for traditional desktop or web applications may not be suitable for mobile app development. The objective of this paper is to propose a framework for mobile app project estimation. The research methodology adopted in this work is based on selecting different features of mobile apps from the SAMOA dataset. These features are later used as input vectors to the selected machine learning (ML) techniques. The results of this research experiment are measured in mean absolute residual (MAR). The experimental outcomes are then followed by the proposition of a framework to recommend an ML algorithm as the best match for superior effort estimation of a project in question. This framework uses the Mamdani-type fuzzy inference method to address the ambiguities in the decision-making process. The outcome of this work will particularly help mobile app estimators, development professionals, and industry at large to determine the required efforts in the projects accurately.

Until now, numerous effort estimation models for software projects have been developed, most of them producing accurate results but not providing the flexibility to decision makers during the software development process. The main objective of this study is to objectively and accurately estimate the effort when using the Scrum methodology. A dynamic effort estimation model is developed by using regression-based machine learning algorithms. Story point as a unit of measure is used for estimating the effort involved in an issue. Projects are divided into phases and the phases are respectively divided into iterations and issues. Effort estimation is performed for each issue, then the total effort is calculated with aggregate functions respectively for iteration, phase and project. This architecture of our model provides flexibility to decision makers in any case of deviation from the project plan. An empirical evaluation demonstrates that the error rate of our story point-based estimation model is better than others.

Software sustainability evaluation has become an essential component of software engineering (SE) owing to sustainability considerations that must be incorporated into software development. Several studies have been performed to address the issues associated with sustainability concerns in the SE process. However, current practices extensively rely on participant experiences to evaluate sustainability achievement. Moreover, there exist limited quantifiable methods for supporting software sustainability evaluation. Our primary objective is to present a methodology that can assist software engineers in evaluating a software system based on well-defined sustainability metrics and measurements. We propose a novel approach that combines machine learning (ML) and software analysis methods. To simplify the application of the proposed approach, we present a semi-automated tool that supports engineers in assessing the sustainability achievement of a software system. The results of our study demonstrate that the proposed approach determines sustainability criteria and defines sustainability achievement in terms of a traceable matrix. Our theoretical evaluation and empirical study demonstrate that the proposed support tool can help engineers identify sustainability limitations in a particular feature of a software system. Our semi-automated tool can identify features that must be revised to enhance sustainability achievement.

Testing multi-thread programs becomes extremely difficult because thread interleavings are uncertain, which may cause a program getting different results in each execution. Thus, Thread Scheduling Sequence (TSS) is a crucial factor in multi-thread program testing. A good TSS can obtain better testing efficiency and save the testing cost especially with the increase of thread numbers. Focusing on the above problem, in this paper, we discuss a kind of approach that can efficiently generate TSS based on the concurrent coverage criteria. First, we give a definition of Synchronization Pair (SP) as well as all Synchronization Pairs Coverage (ASPC) criterion. Then, we introduce the Synchronization Pair Thread Graph (SPTG) to describe the relationships between SPs and threads. Moreover, this paper presents a TSS generation method based on the ASPC according to SPTG. Finally, TSSs automatic generation experiments are conducted on six multi-thread programs in Java Library with the help of Java Path Finder (JPF) tool. The experimental results illustrate that our method not only generates TSSs to cover all SPs but also requires less state number, transition number as well as TSS number when satisfying ASPC, compared with other three widely used TSS generation methods. As a result, it is clear that the efficiency of TSS generation is obviously improved.

Case-based reasoning heavily depends on the structure and content of the cases, and semantics is essential to effectively represent cases. In the field of structured case representation, most of the works regarding case representation and measurement of semantic similarity between cases are based on model-theoretic semantics and their extensions. The purpose of this study is to explore the potential of experienced-grounded semantics in case representation and semantic similarity measurement. The main contents in this study are as follows: (i) a case representation model based on experience-grounded semantic is proposed, (ii) a novel semantic similarity measurement method with multi-strategy reasoning is introduced, and (iii) a case-based reasoning software for urban firefighting field based on the proposed model is designed and implemented. Theoretically, compared with traditional structured case representation methods, the proposed model not only represents case in a fully formalized way, but also provides a novel metric for computing the strength of the semantic relationship between cases. The proposed model has been applied in an intelligent decision-support software for urban firefighting.