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This paper deals with how to determine which features should be included in the software to be developed. Metaheuristic techniques have been applied to this problem and can help software developers when they face contradictory goals. We show how the knowledge and experience of human experts can be enriched by these techniques, with the idea of obtaining a better requirements selection than that produced by expert judgment alone. This objective is achieved by embedding metaheuristics techniques into a requirements management tool that takes advantage of them during the execution of the development stages of any software development project. © 2015 Wiley Periodicals, Inc. Complexity 21: 250–262, 2016

Model-to-model (M2M) transformations are among the key components of model-driven development, enabling a certain level of automation in the process of developing models. The developed solution of using drag-and-drop actions-based M2M transformations contributes to this purpose by providing a flexible, reusable, customizable, and relatively easy-to-use transformation method and tool support. The solution uses model-based transformation specifications triggered by user-initiated drag-and-drop actions within the model deployed in a computer-aided software engineering (CASE) tool environment. The transformations are called partial M2M transformations, meaning that a specific user-defined fragment of the source model is being transformed into a specific fragment of the target model and not running the whole model-level transformation. In this paper, in particular, we present the main aspects of the developed extension to that M2M transformation method, delivering a set of natural language processing (NLP) techniques on both the conceptual and implementation level. The paper addresses relevant developments and topics in the field of natural language processing and presents a set of operators that can be used to satisfy the needs of advanced textual preprocessing in the scope of M2M transformations. Also in this paper, we describe the extensions to the previous M2M transformation metamodel necessary for enabling the solution’s NLP-related capabilities. The usability and actual benefits of the proposed extension are introduced by presenting a set of specific partial M2M transformation use cases where natural language processing provides actual solutions to previously unsolvable situations when using the previous M2M transformation development.

Currently, enterprises have to make quick and resilient responses to changing market requirements. In light of this, low-code development platforms provide the technology mechanisms to facilitate and automate the development of software applications to support current enterprise needs and promote digital transformation. Based on a theory-building research methodology through the literature and other information sources review, the main contribution of this paper is the current characterisation of the emerging low-code domain following the foundations of the computer-aided software engineering field. A context analysis, focused on the current status of research related to the low-code development platforms, is performed. Moreover, benchmarking among the existing low-code development platforms addressed to manufacturing industry is analysed to identify the current lacking features. As an illustrative example of the emerging low-code paradigm and respond to the identified uncovered features, the virtual factory open operating system (vf-OS) platform is described as an open multi-sided low-code framework able to manage the overall network of a collaborative manufacturing and logistics environment that enables humans, applications, and Internet of Things (IoT) devices to seamlessly communicate and interoperate in the interconnected environment, promoting resilient digital transformation.

Software fault prediction aims to identify fault-prone software modules by using some underlying properties of the software project before the actual testing process begins. It helps in obtaining desired software quality with optimized cost and effort. Initially, this paper provides an overview of the software fault prediction process. Next, different dimensions of software fault prediction process are explored and discussed. This review aims to help with the understanding of various elements associated with fault prediction process and to explore various issues involved in the software fault prediction. We search through various digital libraries and identify all the relevant papers published since 1993. The review of these papers are grouped into three classes: software metrics, fault prediction techniques, and data quality issues. For each of the class, taxonomical classification of different techniques and our observations have also been presented. The review and summarization in the tabular form are also given. At the end of the paper, the statistical analysis, observations, challenges, and future directions of software fault prediction have been discussed.

Traditional approaches to software development - the ones embodied in CASE tools and modeling frameworks - are appropriate for building individual software components, but they are not designed to face the challenges of open environments. Service-oriented computing provides a way to create a new architecture that reflects components' trends toward autonomy and heterogeneity. We thus emphasize SOC concepts instead of how to deploy Web services in accord with current standards. To begin the series, we describe the key concepts and abstractions of SOC and the elements of a corresponding engineering methodology.

Despite the acknowledged importance of quantitative security assessment in secure software development, current literature still lacks an efficient model for measuring internal software security risk. To this end, in this paper, we introduce a hierarchical security assessment model (SAM), able to assess the internal security level of software products based on low-level indicators, i.e., security-relevant static analysis alerts and software metrics. The model, following the guidelines of ISO/IEC 25010, and based on a set of thresholds and weights, systematically aggregates these low-level indicators in order to produce a high-level security score that reflects the internal security level of the analyzed software. The proposed model is practical, since it is fully automated and operationalized in the form of a standalone tool and as part of a broader Computer-Aided Software Engineering (CASE) platform. In order to enhance its reliability, the thresholds of the model were calibrated based on a repository of 100 popular software applications retrieved from Maven Repository. Furthermore, its weights were elicited in a way to chiefly reflect the knowledge expressed by the Common Weakness Enumeration (CWE), through a novel weights elicitation approach grounded on popular decision-making techniques. The proposed model was evaluated on a large repository of 150 open-source software applications retrieved from GitHub and 1200 classes retrieved from the OWASP Benchmark. The results of the experiments revealed the capacity of the proposed model to reliably assess internal security at both product level and class level of granularity, with sufficient discretion power. They also provide preliminary evidence for the ability of the model to be used as the basis for vulnerability prediction. To the best of our knowledge, this is the first fully automated, operationalized and sufficiently evaluated security assessment model in the modern literature.

Purpose This systematic review identified, synthesized, and integrated concept analyses on self‐care and related concepts. Design The guidelines for systematic literature reviews of the Joanna Briggs Institute were followed. Methods The Cumulative Index to Nursing and Allied Health Literature (CINAHL), PubMed, PsycINFO, and EMBASE databases were searched for concept analyses published in the past 20 years. Findings A total of 26 concept analyses were identified that had been published on self‐care, self‐care agency, self‐monitoring, self‐management, self‐management support, symptom management, and self‐efficacy. Differences and commonalities in the examined literature were identified, and a model was delineated, explaining the relations among the various concepts from the nursing perspective. Conclusions The healthcare literature has broadly described self‐care and related concepts; however, consensus on the definitions remains beyond our reach and should not be expected, due to the different perspectives and paradigms from which the concepts are interpreted. From a nursing perspective, self‐care can be considered a broad concept encompassing the other concepts, which describe more specific individual levels of activities and processes. Clinical Relevance Nurses are actively involved in disease management and self‐management support as well as in promoting self‐care in healthy and sick people. Referring to a model on self‐care and related concepts could avoid misinterpretations in nursing practice, research, and policy.

ContextSoftware selection in large-scale software development continues to be ad hoc and ill-structured. Previous proposals for software component selection tend to be technology-specific and/or do not consider business or ecosystem concerns.ObjectiveOur main aim is to develop an industrially relevant technology-agnostic method that can support practitioners in making informed decisions when selecting software components for use in tools or in products based on a holistic perspective of the overall environment.MethodWe used method engineering to iteratively develop a software selection method for Ericsson AB based on a combination of published research and practitioner insights. We used interactive rapid reviews to systematically identify and analyse scientific literature and to support close cooperation and co-design with practitioners from Ericsson. The model has been validated through a focus group and by practical use at the case company.ResultsThe model consists of a high-level selection process and a wide range of criteria for assessing and for evaluating software to include in business products and tools.ConclusionsWe have developed an industrially relevant model for component selection through active engagement from a company. Co-designing the model based on previous knowledge demonstrates a viable approach to industry-academia collaboration and provides a practical solution that can support practitioners in making informed decisions based on a holistic analysis of business, organisation and technical factors.

A code clone is a code portion in source files that is identical or similar to another. Since code clones are believed to reduce the maintainability of software, several code clone detection techniques and tools have been proposed. This paper proposes a new clone detection technique, which consists of the transformation of input source text and a token-by-token comparison. For its implementation with several useful optimization techniques, we have developed a tool, named CCFinder (Code Clone Finder), which extracts code clones in C, C++, Java, COBOL and other source files. In addition, metrics for the code clones have been developed. In order to evaluate the usefulness of CCFinder and metrics, we conducted several case studies where we applied the new tool to the source code of JDK, FreeBSD, NetBSD, Linux, and many other systems. As a result, CCFinder has effectively found clones and the metrics have been able to effectively identify the characteristics of the systems. In addition, we have compared the proposed technique with other clone detection techniques.

Programming is a vital skill in computer science and engineering-related disciplines. However, developing source code is an error-prone task. Logical errors in code are particularly hard to identify for both students and professionals, and a single error is unexpected to end-users. At present, conventional compilers have difficulty identifying many of the errors (especially logical errors) that can occur in code. To mitigate this problem, we propose a language model for evaluating source codes using a bidirectional long short-term memory (BiLSTM) neural network. We trained the BiLSTM model with a large number of source codes with tuning various hyperparameters. We then used the model to evaluate incorrect code and assessed the model’s performance in three principal areas: source code error detection, suggestions for incorrect code repair, and erroneous code classification. Experimental results showed that the proposed BiLSTM model achieved 50.88% correctness in identifying errors and providing suggestions. Moreover, the model achieved an F-score of approximately 97%, outperforming other state-of-the-art models (recurrent neural networks (RNNs) and long short-term memory (LSTM)).