Showing papers in "Journal of Systems and Software in 2021"

TL;DR: This systematic literature review oriented to software engineering aims at highlighting current problems and possible solutions concerning smart contracts and blockchain applications development, as well as identifying open challenges that require further research.

TL;DR: A multiple case study with seven large-scale systems companies, reporting their challenges, together with best practices from industry, to derive potential solutions for the challenges and to outline research gaps.

TL;DR: A systematic mapping study about teaching major Software Engineering Trends in project courses reveals that Agile Software Development is the major trend, and points out the possible gaps between Software Industry and Education.

TL;DR: In this article, the authors conducted a systematic literature review of 557 unique papers published until 2020, following a consolidated methodology applied in software engineering to understand what technical debt prioritization approaches have been proposed in research and industry.

TL;DR: In this paper, the authors performed a systematic literature review (SLR) to systematically identify, analyze, summarize, and synthesize the current state of software engineering (SE) research for engineering ML systems.

TL;DR: A critical review on the evaluation of patch generation systems is conducted and eight evaluation metrics for fairly assessing the performance of APR tools are proposed to contribute to boosting the development of practical, and reliably performable program repair tools.

TL;DR: The continued presence of the role of the project manager in agile software projects is highlighted as a part of the transition from traditional to agile ways of working.

TL;DR: This paper combines the N-Gram TF–IDF feature selection with binary and multiclass classifiers to build a new model to automate the classification of refactorings based on their quality improvement categories and reaches an F-measure of up to 90% even with a relatively small training dataset.

TL;DR: Among the metrics investigated, socio-technical congruence, communicability, and turnover-related metrics are the most powerful predictors of the emergence of community smells.

TL;DR: Wang et al. as discussed by the authors employed a hybrid deep neural network (CNN) and kernel extreme learning machine (KELM) to construct a unified defect prediction predictor called WSHCKE, which further integrate the selected features into the abstract deep semantic features by CNN and boost the prediction performance by taking full advantage of the strong classification capacity of KELM.

TL;DR: All investigated variation control systems offer an iterative (checkout–modify–commit) workflow, but there are essential differences affecting developers, which is highlighted and compared.

TL;DR: In this article, the authors proposed a ground truth dataset of pull request and issue comments in 5,000 distinct Github accounts of which 527 have been identified as bots, based on a manual analysis with high interrater agreement.

TL;DR: A review of 94 primary studies showed that accessibility is not characterized as a property of the final software only, Instead, it evolves over the software life cycle, and aims to provide designers and developers with an updated view of methods, tools, and other assets that contribute to process enrichment, valuing accessibility.

TL;DR: The findings from this systematic review show that the potential application objective is important; there are a vast number of case studies reported in the literature from the basis of several domains and software systems and calls to further investigate the synergies between artificial intelligence and software engineering.

TL;DR: It appears that source code reuse is neither a silver bullet to combat vulnerabilities nor a frightening werewolf that entail an excessive number of them, and a strong correlation between a higher number of dependencies and vulnerabilities is found.

TL;DR: This work performed a large-scale empirical study on 40 unsupervised models on an open-source dataset including 27 project versions with 3 types of features to explore the impacts of clustering-based models on defect prediction performance.

TL;DR: This is the largest empirical study of automatic repair on QuixBugs, combining both quantitative and qualitative insights, and is publicly available on GitHub in order to facilitate future research on automatic program repair.

TL;DR: In this article, the authors propose parameterized modal live sequence charts (PMLSCs) for railway control systems, which is a language that is expressive, of reasonable complexity, and easy to understand.

TL;DR: A novel Semantic CNN parser SeCNN is proposed for code comment generation and uses a CNN (Convolutional Neural Network) to alleviate the long-dependency problem and design several novel components to capture the semantic information of the source code.

TL;DR: In this paper, the authors explore the feasibility of applying deep learning models to detect smells without extensive feature engineering and investigate the possibility of applying transfer-learning in the context of detecting code smells.

TL;DR: A GSD Risk Catalog of 63 risks is developed to assess the degree to which two scaling agile frameworks–Disciplined Agile Delivery (DAD) and the Scaled Agile Framework (SAFe)–address software project risks in GSD.

TL;DR: This work considers meetamodels as data points and classify them using supervised learning techniques, and proposes memoCNN as a novel approach to classification of metamodel using convolutional neural network.

TL;DR: The main findings indicate that scientific software developers are focusing on practices that improve implementation productivity, such as code reuse, use of third-party libraries, and the application of “good” programming techniques.

TL;DR: A systematic literature review (SLR) of the existing NeuroSE literature revealed that the number of authors publishing NeuroSE research is still relatively small, yet high quality contributions exist constituting a valuable basis for future studies.

TL;DR: This paper describes the Vitruvius approach for consistency in view-based modeling by formalizing the notion of consistency, presenting languages for consistency preservation, and defining a model-driven development process and shows how existing models can be integrated.

TL;DR: An empirical study to investigate the characteristics of optimization bugs in two mainstream compilers, GCC and LLVM, and reveals that Optimizations are the buggiest component in both compilers except for the C++ component.

TL;DR: A systematic literature review (SLR) that covers 41 SIAs based on software-systems analyses and creates a taxonomy of SIAs and builds a multi-layer classification of existing identification approaches that helps practitioners in selecting a suitable approach for identifying services in legacy software systems.

TL;DR: It is shown that in addition to the identification of modules, the deployment and communication approaches are equally crucial for a successful application of the microservice architecture style.

TL;DR: This work investigates whether code quality issues such as code smells, antipatterns, and coding style violations in the pull request code affect the chance of its acceptance when reviewed by a maintainer of the project.

TL;DR: SLAMIG as mentioned in this paper is a set of algorithms that composes deadline-aware multiple migration grouping algorithm and on-line migration scheduling to determine the sequence of VM/VNF migrations.