Software requirements testing approaches: a systematic literature review

Requirements Engineering - Testing a software system is an important step approach to ensuring quality, safety, and reliability in safety-critical systems (SCS). Several authors have published new...     

Knowledge-based approaches in software documentation: A systematic literature review

ContextSoftware documents are core artifacts produced and consumed in documentation activity in the software lifecycle. Meanwhile, knowledge-based approaches have been extensively used in software development for decades, however, the software engineering community lacks a comprehensive understanding on how knowledge-based approaches are used in software documentation, especially documentation of software architecture design.ObjectiveThe objective of this work is to explore how knowledge-based approaches are employed in software documentation, their influences to the quality of software documentation, and the costs and benefits of using these approaches.MethodWe use a systematic literature review method to identify the primary studies on knowledge-based approaches in software documentation, following a pre-defined review protocol.ResultsSixty studies are finally selected, in which twelve quality attributes of software documents, four cost categories, and nine benefit categories of using knowledge-based approaches in software documentation are identified. Architecture understanding is the top benefit of using knowledge-based approaches in software documentation. The cost of retrieving information from documents is the major concern when using knowledge-based approaches in software documentation.ConclusionsThe findings of this review suggest several future research directions that are critical and promising but underexplored in current research and practice: (1) there is a need to use knowledge-based approaches to improve the quality attributes of software documents that receive less attention, especially credibility, conciseness, and unambiguity; (2) using knowledge-based approaches with the knowledge content in software documents which gets less attention in current applications of knowledge-based approaches in software documentation, to further improve the practice of software documentation activity; (3) putting more focus on the application of software documents using the knowledge-based approaches (knowledge reuse, retrieval, reasoning, and sharing) in order to make the most use of software documents; and (4) evaluating the costs and benefits of using knowledge-based approaches in software documentation qualitatively and quantitatively.     

A systematic literature review on web service clustering approaches to enhance service discovery,selection and recommendation

With the advancement of web 2.0 and the development of the Internet of Things (IoT), all tasks can be handled with the help of handheld devices. Web APIs or web services are providing immense power to IoT and are working as a backbone in the successful journey of IoT. Web services can perform any task on a single click event, and these are available over the internet in terms of quantity, quality, and variety. It leads to the requirement of service management in the service repository. The well-managed and structured service repository is still challenging as services are dynamic, and documentation is limited. It is also not a piece of cake to discover, select and recommend services easily from a pool of services. Web service clustering (WSC) plays a vital role in enhancing the service discovery, selection, and recommendation process by analyzing the similarity among services. In this paper, with a systematic process total of 84 research papers are selected, and different state-of-the-art techniques based on web service clustering are investigated and analyzed. Furthermore, this Systematic Literature Review (SLR) also presents the various mandatory and optional steps of WSC, evaluation measures, and datasets. Research challenges and future directions are also identified, which will help the researchers to provide innovative solutions in this area.     

Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review

Multimedia Tools and Applications - Since early 2020, Coronavirus Disease 2019 (COVID-19) has spread widely around the world. COVID-19 infects the lungs, leading to breathing difficulties. Early...     

A systematic review of approaches for testing concurrent programs

Concurrent programs are replacing the sequential programs as they utilize the true capabilities of multicore architecture. The extensive use of multicore systems and multithreaded paradigms warrants more attention to the testing of the concurrent programs. The testing concurrent program is not a new field as it has been more than 40 years because the first problem related to the testing concurrent program was addressed by the researchers. The field covers various domains, which include concurrency problems, testing approaches, techniques, graphical representations, tools, and subject systems. This paper aims at providing an overview of research in the domain of testing concurrent programs by classifying it into eight categories: (a) reachability testing, (b) structural testing, (c) model‐based testing, (d) mutation‐based testing, (e) slicing‐based testing, (f) formal methods, (g) random testing, and (h) search‐based testing. The survey is focused on the techniques applied, methodologies followed, and tools used in these aforementioned approaches. Furthermore, the gaps are also identified in different approaches. The paper concludes with the consolidation of various testing parameters along with the future directions. Copyright © 2015 John Wiley & Sons, Ltd.     

Correction to: Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review

Multimedia Tools and Applications -     

网格环境中资源发现机制的研究

随着高性能应用需求的迅猛发展,单台高性能计算机已经不能胜任一些超大规模应用问题的解决。这就需要将地理上分布、系统异构的多种计算资源通过高速网络连接起来,共同解决大型应用问题,从而产生了网格（Grid）。但是在网格这种共享资源的集合中,由于资源共享的广泛性,资源的异构性等等都使得资源发现更具有挑战性。目前存在的一些分布式环境中的资源发现大多是基于集中获分布的,由于集中式资源发现不能很好地满足系统的扩充,而分布式资源发现又会产生大量的网络通讯,因此这两种机制将不可避免地结合在一起,即形成分层式资源发现机制。     

Data-driven approaches to empirical discovery

In this paper we track the development of research in empirical discovery. We focus on four machine discovery systems that share a number of features: the use of data-driven heuristics to constrain the search for numeric laws; a reliance on theoretical terms; and the recursive application of a few general discovery methods. We examine each system in light of the innovations it introduced over its predecessors, providing some insight into the conceptual progress that has occurred in machine discovery. Finally, we reexamine this research from the perspectives of the history and philosophy of science.     

A systematic review of evaluation of variability management approaches in software product lines

Context

Variability management (VM) is one of the most important activities of software product-line engineering (SPLE), which intends to develop software-intensive systems using platforms and mass customization. VM encompasses the activities of eliciting and representing variability in software artefacts, establishing and managing dependencies among different variabilities, and supporting the exploitation of the variabilities for building and evolving a family of software systems. Software product line (SPL) community has allocated huge amount of effort to develop various approaches to dealing with variability related challenges during the last two decade. Several dozens of VM approaches have been reported. However, there has been no systematic effort to study how the reported VM approaches have been evaluated.

Objective

The objectives of this research are to review the status of evaluation of reported VM approaches and to synthesize the available evidence about the effects of the reported approaches.

Method

We carried out a systematic literature review of the VM approaches in SPLE reported from 1990s until December 2007.

Results

We selected 97 papers according to our inclusion and exclusion criteria. The selected papers appeared in 56 publication venues. We found that only a small number of the reviewed approaches had been evaluated using rigorous scientific methods. A detailed investigation of the reviewed studies employing empirical research methods revealed significant quality deficiencies in various aspects of the used quality assessment criteria. The synthesis of the available evidence showed that all studies, except one, reported only positive effects.

Conclusion

The findings from this systematic review show that a large majority of the reported VM approaches have not been sufficiently evaluated using scientifically rigorous methods. The available evidence is sparse and the quality of the presented evidence is quite low. The findings highlight the areas in need of improvement, i.e., rigorous evaluation of VM approaches. However, the reported evidence is quite consistent across different studies. That means the proposed approaches may be very beneficial when they are applied properly in appropriate situations. Hence, it can be concluded that further investigations need to pay more attention to the contexts under which different approaches can be more beneficial.     

Boosting algorithms in energy research: a systematic review

Neural Computing and Applications - Machine learning algorithms have been extensively exploited in energy research, due to their flexibility, automation and ability to handle big data. Among the...     

A systematic review of transformation approaches between user requirements and analysis models

Model transformation is one of the basic principles of Model Driven Architecture. To build a software system, a sequence of transformations is performed, starting from requirements and ending with implementation. However, requirements are mostly in the form of text, but not a model that can be easily understood by computers; therefore, automated transformations from requirements to analysis models are not easy to achieve. The overall objective of this systematic review is to examine existing literature works that transform textual requirements into analysis models, highlight open issues, and provide suggestions on potential directions of future research. The systematic review led to the analysis of 20 primary studies (16 approaches) obtained after a carefully designed procedure for selecting papers published in journals and conferences from 1996 to 2008 and Software Engineering textbooks. A conceptual framework is designed to provide common concepts and terminology and to define a unified transformation process. This facilitates the comparison and evaluation of the reviewed papers.     

Competence-based recommender systems: a systematic literature review

ABSTRACT

Competence-based learning is increasingly widespread in many institutions since it provides flexibility, facilitates the self-learning and brings the academic and professional worlds closer together. Thus, the competence-based recommender systems emerged taking the advantages of competences to offer suggestions (performance of a learning experience, assistance of an expert or recommendation of a learning resource) to the user (learner or instructor). The objective of this work is to conduct a new Systematic Literature Review (SLR) concerning competence-based recommender systems to analyse in relation to their nature and assessment of competences an others key factors that provide more flexible and exhaustive recommendations. To do so, a SLR research methodology was followed in which 25 competence-based recommender systems related to learning or instruction environments were classified according to multiple criteria. We evaluate the role of competences in these proposals and enumerate the emerging challenges. Also a critical analysis of current proposals is carried out to determine their strengths and weakness. Finally, future research paths to be explored are grouped around two main axes closely interlinked; first about the typical challenges related to recommender systems and second, concerning ambitious emerging challenges.     

Automatic software refactoring: a systematic literature review

Refactoring a software artifact is an embedded task in the maintenance phase of the software life cycle. To reduce the time and effort required for this task, researchers proposed methods to automate the software refactoring process at the design and code levels. In this paper, we conducted a systematic literature review of papers that suggest, propose, or implement an automated refactoring process. Using different phases, setting several quality measures, and snowballing, only 41 papers passed to the last stage to be analyzed and reviewed. We observe an increase in the number of papers that propose automatic refactoring. The results show that while most of the papers discuss code refactoring, only a few recent papers are focused on model refactoring. Search-based refactoring is gaining more popularity, and several researchers have used it to perform refactoring in a quick and efficient manner.     

OMR metrics and evaluation: a systematic review

Multimedia Tools and Applications - Music is rhythm, timbre, tones, intensity and performance. Conventional Western Music Notation (CWMN) is used to generate Music Scores in order to register music...     

Quantitative proteomic approaches for biomarker discovery

The rapid advances in proteomic technologies have made possible systematic analysis of hundreds to thousands of proteins in clinical samples with the promise of uncovering novel protein biomarkers for various disease conditions. We will discuss in this review article current MS and protein chip-based quantitative proteomic approaches and their application in biomarker discovery. The emphasis will be placed on new quantification strategies employing stable isotopic labeling coupled with MS/MS, and antibody-based protein chips and nanodevices. The strength and weakness of each technology are briefly highlighted.     

Data management in digital twins: a systematic literature review

Knowledge and Information Systems - The Internet of Things (IoT) and continuous advances in data-gathering devices and techniques have significantly increased the amount of relevant data that can...     

Evaluating expert systems: a review of applicable approaches

This paper surveys various approaches for evaluating expert systems. More specifically, we categorize the applicable methods found in the literature as being qualitative, quantitative or a hybrid. The more seminal of these methods are described. The review is primarily intended for an audience of researchers and practitioners interested in understanding the scope and limitation of methods within each of the three approaches.