International Journal of Software Engineering and Knowledge Engineering

Integrated Development Environments (IDEs) are pivotal in enhancing productivity with features like code completion in modern software development. Recent advancements in Natural Language Processing (NLP) have empowered neural language models for code completion. In this study, we present an extensive investigation of the impact of open and closed vocabulary systems on the task of code completion. Specifically, we compare open and closed vocabulary systems with various vocabulary sizes to observe their impact on code completion performance. We experiment with three different open vocabulary systems: byte pair encoding (BPE), WordPiece and Unigram to compare them with closed-vocabulary systems to analyze their modeling performance. We also conduct experiments with different context sizes to study their impact on code completion performance. We have experimented using various prominent language models, including one from recurrent neural networks and five from transformers. Our results indicate that vocabulary size significantly impacts modeling performance and can artificially boost the accuracy of code completion models, especially in the case of a closed-vocabulary system. Moreover, we find that different vocabulary systems have varying impacts on token coverage, whereas open-vocabulary systems exhibit better token coverage. Our findings offer valuable insights for building effective code completion models, aiding researchers and practitioners in this field.

Scrum has emerged as the most widely used and desired Agile approach for providing corporate strategic competency by establishing a solid foundation for project management. However, there are several issues confronted during its implementation. Some researchers tried to solve specific areas of Scrum issues except only research that covers several aspects without resolving all of them. So, this study presents the EFSP model for improving maintainability, security and reusability. Methodologically, in this study, we carry out the following tasks: (i) apply Mark or 7C model on requirements, and (ii) identify Scrum aspects (artifacts and/or activities) that should be expanded as follows: adding the concept of systematic reusability into sprint planning, classifying the requirements into four layers according to clean architecture into sprint backlog, and evolutionary model into sprint. This model offers solutions to these problems while maintaining the simplicity and flexibility of Scrum. The system evaluation results have achieved an improvement in maintainability by reducing technical debt from 1.6% to 0.9%, security from 10 to 3, timeliness from 5 to 2, and improving team productivity from 1.24 to 2.78. The EFSP model may be utilized to develop a standard in other projects.

Pointer analysis is the underlying technique of many static analysis tools for vulnerability discovery. It has proved to be effective in identifying a variety of vulnerabilities, such as buffer overflow vulnerabilities and injection vulnerabilities. However, most existing pointer analysis approaches require whole-program availability, i.e. the program to be analyzed should be complete, which may hinder a timely analysis during the coding phase. In this paper, we present two approaches, exhaustive and demand-driven pointer analyses, both of which are applied to a paradigm known as Human–Machine Pair Programming. The ideas enable us to discover security flaws as early as in the coding phase. In this paper, we describe in detail how our approaches maintain flow sensitivity and propagate points-to and taint information in an incremental fashion. We conduct an evaluation of our approaches on SecuriBench Micro and show that the approaches can capture all the potential vulnerabilities in the test cases, though several false alarms are reported.

Traditionally, drinking water supply infrastructures have been designed to store as much water as possible and to do so during the energy cheap hours. This approach is unsustainable today. The use of digital systems capable of modeling the behavior of infrastructures and the creation of intelligent control systems can help to make drinking water supply systems more efficient and effective, while still meeting minimum service requirements. This work proposes the development of a control system, based on multi-agent systems (MAS), capable of generating an intelligent control over a drinking water infrastructure, based on the use of local interests of the agents and with an emergent behavior coherent with the needs. To validate the proposal, a simulator based on the infrastructures of a medium-sized Spanish city of 5000 inhabitants has been built and the control has been simulated using the MAS. The results show how the system can maintain the objectives set, handling unknown situations, and facilitating the development of future physical systems based on a just-in-time paradigm that guarantees sustainability, as it allows the generation of virtualizations of the infrastructures and their behavior, thus being able to study the best option for an infrastructure to resolve a supply situation.

Session-Based Recommendation (SBR) systems are facing considerable challenges, with their primary objective being to implement precise recommendations based on users’ historical behavior sequences. Graph Neural Networks (GNNs) have emerged as powerful tools for processing graph-structured data in recommendation systems. Although recent research has advanced in this area, a significant gap remains in the in-depth exploration of transitional relationships between user interests. Additionally, real-world recommendation scenarios typically involve various heterogeneous relationships, which contain a wealth of information that can significantly enhance the learning of user preferences. To address this research gap, in this paper, we introduce a model termed the Sentiment-Time Heterogeneous Residual Graph Attention Transformer (STH-ResGAT), which is designed to capture the dynamic nature of user interests and the complexity inherent in heterogeneous graphs. In STH-ResGAT, we develop a Sentiment-Time-Heterogeneous Graph (STH-Graph) that integrates sentiment and time factors into the edges of the graph structure. Furthermore, the Residual Graph Attention Transformer for Heterogeneous Networks (ResGAT-Het) is designed to manage diverse node and edge types based on the STH-Graph. Extensive experiments are conducted on four widely-used benchmark datasets: Ciao, Yelp, Epinions and LibraryThing. The results demonstrate that our proposed STH-ResGAT method significantly outperforms previous state-of-the-art baseline approaches.The implementation of ResGAT-Het is available in https://github.com/zhangyu2234/ResGAT-Het.git.

Entity alignment (EA) aims to find equivalent entities in knowledge graphs (KGs) from multiple data sources and is a crucial step in integrating KGs. Recent studies learn the similarity of entity embeddings by aggregating neighboring entities. However, these methods solely compare neighboring entities and do not incorporate the connected relation between an entity and its neighbors. In this paper, we propose a novel Entity and Relation joint Interaction Learning (ERIL) approach, which effectively captures the interaction between entities and relations, enhancing the precision of alignment across different KGs. Specifically, the ERIL model jointly learns the neighborhood features of entities and the spatial structure of relations to train a shared permutation matrix, capturing comprehensive associative relations within KGs. Moreover, a semi-supervised iterative framework is designed to leverage the positive interactions between entities and relations to identify more aligned entities. Extensive experiments are conducted on five benchmark datasets to demonstrate the effectiveness of ERIL compared with existing state-of-the-art EA methods. On DBP15K, our model ERIL outperforms currently available EA methods by 1.9% on Hits@10.