Showing papers in "Journal of Systems and Software in 2023"

TL;DR: In this paper , the authors systematically identified, analyzed, summarized, and synthesized the current state of the utilization of DL algorithms for SDP in the literature, and systematically selected a pool of 102 peer-reviewed studies and then conducted a quantitative and qualitative analysis using the data extracted from these studies.

TL;DR: Wang et al. as discussed by the authors proposed an FII pattern mining algorithm covering the main features of SoS interaction logs, an overlapping clustering technique for multiple pattern mining, and a pattern-based fault localization method.

TL;DR: In this article , the authors apply a grounded theory approach to examine the role of KBRs in Agile Software Development (ASD) and develop the Knowledge-Push theory, which explains how K-based Resources (KBRs) boost the need for change in ASD.

TL;DR: In this article , the authors conducted a systematic literature review with 37 primary studies to understand the state-of-the-art of blockchain governance, and provided actionable guidelines for academia and practitioners to use throughout the lifecycle of blockchain, and discuss future trends to support researchers in this area.

TL;DR: In this paper , the authors present definitions for IoT and CPS that reflect the core intuition of the terms as found in the literature review while providing a clear demarcation of the two terms.

TL;DR: In this paper , the authors compared the performance and functionality of Deep Learning based solutions to human programmers on a set of programming tasks, including sorting and implementing data structures, and found that the correct ratio of humans' solutions is greater than Copilot's suggestions, while the buggy solutions generated by Copilot require less effort to be repaired.

TL;DR: In this article , the authors propose a model-driven approach for automated mode switching of software systems to support system administrators in dealing with vulnerabilities and reducing the risk of exposure by using a multi-modal architecture.

TL;DR: In this paper , the authors used a two-phased multi-Method study to investigate how working from home impacted Scrum practitioners through semi-structured interviews, and then they proposed a theoretical model that they test and generalize using Partial Least Squares-Structural Equation Modeling (PLS-SEM).

TL;DR: Wang et al. as discussed by the authors proposed a GNN based approach for smart contract vulnerability detection, which combines abstract syntax tree (AST), control flow graph (CFG), and program dependency graph (PDG).

TL;DR: In this paper , the authors present a process for systematically measuring IRR/IRA in GT studies, when appropriate, which is grounded in a previous systematic mapping study on collaborative GT in the field of software engineering.

TL;DR: In this paper , the authors looked into how explainability might help to tackle this issue and created privacy explanations that aim to help to clarify to end users why and for what purposes specific data is required.

TL;DR: In this paper , a large study over 8 configurable systems that quantifies the existing interactions between input data and configurations of software systems is presented. And the results demonstrate that inputs fed to software systems can interact with their configuration options in non-monotonous ways, significantly impacting their performance properties.

TL;DR: In this article , the authors conducted a systematic literature review (SLR) of 47 selected studies from the most relevant scientific digital libraries covering pre-migration, migration, and postmigration stages, and provided the primary motivation for cloud migration, existing cloud migration frameworks, targeted migration architecture patterns, and migration challenges.

TL;DR: CCStokener as mentioned in this paper proposes to enhance the detection capability of token-based tools via complementing the traditional token with semantic information such as the structural information around the token and its dependency with other tokens in form of n-grams.

TL;DR: In this paper , the authors present an evidence-based roadmap for IoT development to support developers in specifying, designing, and implementing IoT systems, based on experimental studies to acquire evidence to define the IoT Roadmap.

TL;DR: Zhang et al. as mentioned in this paper proposed a quality prediction-based filter for automatic issue title generation based on the deep learning module and the information retrieval module, which can automatically filter this issue and return a warning message.

TL;DR: Wang et al. as discussed by the authors proposed a deep learning-based approach, CSGVD (Combining Sequence and Graph embedding for Vulnerability Detection), which considers function-level vulnerability detection as a graph binary classification task.

TL;DR: In this article , the authors present a tool-supported technique to automatically extract DFDs from the implementation code of microservices, using a technique that iteratively detects new keywords, thereby snowballing through an application's codebase.

TL;DR: Wang et al. as discussed by the authors proposed a Graph Collaborative Filtering-based bug triaging framework to capture the bug-developer correlations in bug-tagging activities, which is modeled as a bipartite graph, and natural language processing-based pre-training is implemented on bug reports to initialize bug nodes.

TL;DR: In this paper , the authors explored the use of gamification and investigated whether this technique can help to improve the engagement and performance of software testing students, and found that the students that participated in the gamification experience were more engaged and achieved a better performance.

TL;DR: In this article , the authors conducted a case study investigating the application of requirements models at Ericsson AB, a Swedish telecommunications company, in order to understand how requirements relate to each other, and to keep track with the product's evolution.

TL;DR: In this paper , a systematic mapping study dealing with recent research recovering these links through information retrieval, with a particular focus on natural language processing (NLP), was performed, covering a period from 2013 to 2021, and a trend analysis on NLP techniques and tools involved, and traceability efforts (applying NLP) across the software development life cycle (SDLC).

TL;DR: In this paper , an approach to runtime safety enforcement of software systems based on the MAPE-K control loop architecture for system monitoring and control, and on the State Machine as runtime model to specify safety assertions and enforcement strategies for steering the correct system behavior is presented.

TL;DR: In this article , the authors present a taxonomy for continuous development of AI models, which consists of four stages: Data Handling, Model Learning, Software Development, and System Operations.

TL;DR: In this paper , the authors investigate why people choose to stream themselves programming and if they perceive themselves improving their programming skills by live streaming, finding that the motivations to stream included accountability, self-education, community, and visibility of the streamers' work, and streamers perceived a positive influence on their ability to write source code.

TL;DR: In this article , the authors present a case study in which they exploited symbolic execution to automatically generate test cases for a set of safety-critical railway software written in Scade, and they report on their experience of using this test generator for testing a subset of Scade programs that belong to the development of an on-board signaling unit for high-speed rail.

TL;DR: In this paper , the authors identify acceptance models and theories and discuss their applicability in the research of acceptance behavior related to software development, and discuss acceptance behavior from the human information processing perspective of automatic and affect-driven processes.

TL;DR: In this article , the authors propose a mathematical model for system architectures, which is scalable and supports co-evolution of different aspects for example of an AI system, based on Design Science Research (DSR).

TL;DR: ARIST as mentioned in this paper combines program analysis (PA), language models (LMs), and several features specialized for the recommendation task which consider the functionality of formal parameters and the positional information of code elements in the given context.