International Journal of Software Engineering and Knowledge Engineering

The dire circumstances presented by the COVID-19 pandemic have had a severely debilitating global impact on education, and led to an urgent transition from the onsite environment (OSE) to the online environment (OLE) for teaching and learning. In that regard, this paper describes the experiences of us and students of our involvement in oral and written tests in multiple software engineering-related courses during 2020 and 2021. The challenges encountered along with the interventions are discussed, and educational lessons based on the reactions and responses of the students are given. The results of a preliminary survey of the students of their learning experience in the OLE are presented and, related to it, the comments from the students highlighting their preferences of the OSE or the OLE are included. The test procedures, processes, and/or practices herein are, in principle, generalizable and potentially applicable to other courses in computer science or software engineering, during emergency remote teaching or even otherwise.

Nowadays, online learning is becoming more and more popular. Various online learning platforms provide a huge amount of learning resources for learners around the world. When choosing or sorting learning resources, learners often need to know what important knowledge concepts are addressed in each learning resource. Exploring the prerequisite relations among concepts is of great significance to educational planning. In this paper, we extracted concepts from the content of course descriptions and proposed a new approach that uses both course-based features and Wikipedia-based features to discover the prerequisite relations between knowledge concepts. Experiments on both English and Chinese datasets show that the proposed method outperforms existing baselines.

Software development with its unique characteristics having knowledge-intensive and human-oriented aspects and complex domains, challenges organizations. The timely outcomes with high quality and desired cost that directly affect customer satisfaction have an important place in many organizations, including banks. In the last decade, as an emerging technique for business processes management, process mining has been applied in many domains, including manufacturing, supply chain, government, healthcare, and software engineering. There are limited number of studies on process mining techniques carried out for the software process, especially in the banking sector. A lack of tool infrastructure enabling to run the entire software development process and the challenges in integrating processed data from separated varying tools and assets complicate the use of process mining for software processes. This paper aims to identify the improvement points in the software development process of the Kuveyt Turk Participation Bank in Turkey through the surfacing actions. The findings and results are gathered by the application of process mining techniques of bupaR, and evaluation is provided by experts in the bank. After that, the relevant process improvements are identified. The results of this paper show that using process mining provides the organization with beneficial results, in particular, and a comprehensive view of the end-to-end Software Development Life Cycle (SDLC) processes.

Autonomous driving vehicles are a kind of typical cyber-physical systems integrating complex interactions between hardware and software components such as collaborative computation, distributed communication, and spatio-clock synchronous control with surrounding traffic environment. They can percept the environment, communicate with surroundings, and react fast enough to control independently. The purpose of autonomous driving emergence is to improve driving safety, reduce environmental pollution, and ease the traffic congestion. However, new features with surrounding open and dynamic environment make systems design and verification becoming more and more complex than ever, such as stochastic communication delay, hardware spontaneous failure distribution, and natively hybrid behaviors described by ordinary differential equations. Spatial and time collision avoidance remains crucial obstacles on the path to becoming ubiquitous and dependable. In this paper, we adopt statistical model checking (SMC) to enlighten possible hazards affected by stochastic and hybrid features in the design phase of autonomous driving systems. In order to provide safety and accountability, we first propose a dedicated multi-lane spatio-clock stochastic specification language (MLSCL) to describe safety invariants and guards in domain-specific autonomous driving systems. Then, we present the semantic mapping rules between MLSCL and UPPAAL SMC models, and design the spatio-clock stochastic and hybrid automata based on MLSCL in order to model inherently stochastic and hybrid behaviors. Finally, we present an illustrative lane-change case study to verify spatio-clock stochastic and hybrid-related properties adopting SMC, and demonstrate the effectiveness of our proposed approach.

Gradle is one of the widely used tools to automatically build a software project. While developers execute the Gradle build for projects, they face various build errors in practice. However, fixing build errors is not easy because developers should manually find out the cause of the build error and its resolution on their project. For this reason, developers spend much time fixing them, and especially it can be worse if a developer lacks the experience of handling build errors. To address this issue, we propose a novel approach named Gradle-AutoFix to automatically fix build errors along with providing their causes and resolutions. In this approach, we collect build errors to group their causes and resolutions and then generate feature vectors from build error messages by applying Bag-of-Word (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Bigram, and an embedding layer. The feature vectors are utilized for training two classification models on cause and resolution. Next, we analyze fixing patterns and define seven resolution rules to fix the build error automatically. Based on our trained models and defined resolution rules, we built Gradle-AutoFix. For the evaluation, we measured how appropriately Gradle-AutoFix provides causes of build errors and resolutions. As a result, we obtained 96% and 91% accuracy, respectively. Also, we assessed how properly Gradle-AutoFix fixes the project’s build error based on the seven resolution rules. The outcome showed a 64.5% build error resolution rate for 231 projects.

Formal specifications can provide a solid foundation for software development and support for techniques of software quality assurance, such as specification-based inspection and testing. To ensure that these techniques can be applied effectively in practice, efficiently and accurately understanding specifications becomes extremely important. While this may be relatively easy for well-trained developers in formal methods, it can be rather difficult for computer since computer does not easily understand specifications. This difficulty poses a challenge for realizing automatic specification-based verification techniques that are in high demand for reducing development cost and improving software reliability. In this paper, we address this problem by discussing how the formal specification can be transformed into a knowledge graph to provide comprehensible, well-organized details of the specification for developers and computers. The transformation is done by extracting and storing information about attributes of each component and by establishing relationships between components in a formal specification. We elaborate on a top-down approach of constructing a knowledge graph from a specification, including creating an ontology, designing the Entity–Relationship (ER) diagram of the relational database based on the created ontology, extracting and storing attribute and relationship information in the relational database, mapping ontology to its instances and relational data to RDF triples, and displaying knowledge graph. Further, we present a case study to show how our approach works on the formal specification of an ATM system. Finally, we describe three experiments to evaluate its performance in improving specification readability, effectively guiding inspectors to establish traceability links between specifications and programs, and detecting defects through program inspection, respectively.