Context: Configuration differences between development and production environments can lead to system failures, data loss, and security vulnerabilities. Practitioners have acknowledged the need to handle these differences. This study aims to provide insights and best practices for mitigating configuration differences between development and production environments in software development through a rapid review (RR) of Grey Literature (GL). Methods: The research method for data collection involves a Rapid Review of the GL. It also employs a thematic analysis to understand the collected data. Results: The study identified nine strategies that help practitioners to mitigate configuration differences between development and production environments, including credentials of access. Examples of these strategies include Automated Deployment Pipeline, Configuration Management Plan (CMP), and PaaS Deployment. We also provide insights into how these strategies contribute to reducing the risk of configuration-related issues and ensuring the smooth and reliable operation of software systems. Additionally, it suggests potential cost-saving methods for mitigating configuration differences. Conclusions: This study emphasizes the importance of understanding the strategies employed to mitigate the risks associated with managing configuration differences in software development projects. By delivering practical guidance and insights, this study has the potential to help IT operations and software development projects to have a deeper comprehension of software configuration management strategies and best practices.

Appraisal of the Completeness of OSMI-based Software by using Software Configuration Management

With the encouragement of policies and the rapid development of the biopharmaceutical industry, the number of software as medical device (SaMD) registration applications in Shanghai has continued to increase in recent years, and this paper summarizes the GMP nonconformities found in the field inspection of SaMD in Shanghai from 2020 to 2023, and the results show that nearly 70% of the problems were found in the software development process. Through in-depth analysis, this paper proposes the corresponding countermeasures for the problems found in the five most common stages such as software requirements, software design, software testing, software defect management and software configuration management, combined with the characteristics of software development. These suggested measures have certain reference significance for medical device software development and quality control personnel, and technical reviewer and inspectors.

The article proposes its own method of providing high availability for configuration management software. The current state of the electronic resources management sphere was examined, the reasons for the use of automation tools were provided. The advantages of using configuration management software were analyzed, examples of using Infrastructure as Code and GitOps approaches to automate the deployment and scaling of electronic resources were given. The existing methods of ensuring high availability were analyzed. The development of our own method of ensuring high availability was carried out. The resulting method of providing high availability is based on the Raft consensus algorithm and the software system clustering approach and extends them with its own solutions. The algorithm of the proposed method was developed, the resulting flowchart of the algorithm and individual steps of its implementation were described in detail. The efficiency of the developed method was evaluated. An a priori ranking of a number of factors that evaluate the effectiveness of automatic recovery strategies and methods was conducted. The analysis of the results has shown that the proposed method implements the most important factors for experts, and in terms of RTO and RPO, the method can work on a par with existing popular disaster recovery strategies.

In the realm of software project development, it's not uncommon for changes to emerge at various stages of a project's lifecycle. Such alterations can manifest in virtually every facet of the software development process, from conceptual design decisions down to the minutiae of the source code. To proficiently manage and track these dynamic changes, professionals turn to a specialized toolset known as Software Configuration Management (SCM). One of the standout features that SCM tools bring to the table is the 'diff' capability. This functionality allows developers to identify and review the disparities between two versions of source code. Recognizing the importance and utility of this feature, the primary objective of this research is to create an advanced diff application. This application, by leveraging the Myers Diff algorithm, is meticulously designed to pinpoint and showcase differences in characters between two sets of text-based data. Moreover, it accentuates these differences by visually highlighting the contrasting characters between the two datasets. To ensure the reliability and accuracy of this newly developed tool, we undertook a series of validation tests. We juxtaposed the results from our application against those from a comparable existing tool. Impressively, the discrepancies in results were minimal, with a marginal difference of just 1%. This suggests not only the utility but also the precision of our application in real-world software development scenarios.

Software Configuration Management (SCM) involves tracking similarities/changes during software evolution. Efficient comparison for tracking requires two perspectives—Granularity: comparing the entities at file level, class level, and method level. Second, Robustness should be prominent to detect renaming and shifting that occur as a part of restructuring. Even GIT repository, which is widely used, allows such comparison with renaming and shifting details but is limited to file level only, along with its own limitation of default similarity criteria of above 50%. In this study, the proposed technique named SBCDetector detects similarity/change status with both perspectives that is lacking in the existing literature. Result shows that one-fourth of entities have been found renamed/shifted at three granularities for eight subject systems improving tracking, understandability, and onboarding. Hybrid technique involving fuzzy logic derives classification model with .99 f-score to detect first- and second-order similarity/change.

With the increasing demand for customized systems and rapidly evolving technology, software engineering faces many challenges. A particular challenge is the development and maintenance of systems that are highly variable both in space (concurrent variations of the system at one point in time) and time (sequential variations of the system, due to its evolution). Recent research aims to address this challenge by managing variability in space and time simultaneously. However, this research originates from two different areas, software product line engineering and software configuration management, resulting in non-uniform terminologies and a varying understanding of concepts. These problems hamper the communication and understanding of involved concepts, as well as the development of techniques that unify variability in space and time. To tackle these problems, we performed an iterative, expert-driven analysis of existing tools from both research areas to derive a conceptual model that integrates and unifies concepts of both dimensions of variability. In this article, we first explain the construction process and present the resulting conceptual model. We validate the model and discuss its coverage and granularity with respect to established concepts of variability in space and time. Furthermore, we perform a formal concept analysis to discuss the commonalities and differences among the tools we considered. Finally, we show illustrative applications to explain how the conceptual model can be used in practice to derive conforming tools. The conceptual model unifies concepts and relations used in software product line engineering and software configuration management, provides a unified terminology and common ground for researchers and developers for comparing their works, clarifies communication, and prevents redundant developments.

In analyzing project regressions, automation has emerged as a major agenda in managing changes in software which requires minimum manual intervention. For rapid testing environment, software development processes such as Agile, Scrum, and XP processes depend on continuous integration tools. There is no single tool to handle the project automation, and the main challenge is dependency on multiple tools. The proposed automation tool should support configuration, execution, and debugging facility. Integrating the project automation works such as software configuration management tools Mercurial and Git, job scheduling tools like Jenkins and Apache Continuum, test management tools like TestNG and Selenium need tight integration which is a challenge. The existing PKI infrastructure for access control does not share data among the software tools and processes increasing the complexity when an organization needs to leverage the existing cloud services. The proposed approach optimizes the execution time by taking single CSV with input test case and metadata information and efficiently group and executes the tests automatically. The proposed method includes implementation of security access control mechanism for the jobs execution platform in cloud environment.

Software product lines (SPL) are used in industry to achieve more efficient software development. However, efficient configuration management system is crucial for the success of any SPL. Very few approaches are found on software configuration management (SCM) in SPL. This study aims at surveying existing research on SCM in SPL in order to identify useful approaches and needs for future research. A systematic mapping study was conducted to find as much literature as possible between 2010-2020, and the twenty three papers found were classified according to context, research and contribution type. Most research effort is spent on product variability (87%) with contributions such as proposed tools and models (70%), sketched out in detail but not yet evaluated, i.e. solution proposals (63.33%). According to the results found, more validation and evaluation research of the SCM is needed to provide a better foundation for SPL.

With the increasing demand for software systems, the software development industry is also developing rapidly. With the development of information technology, the more functions of the software, the more valuable it is, so the function design of the software becomes more complicated and difficult. The design of software system functions is increasingly large and complex. Scientific and effective use of software configuration management can well deal with collaborative work problems such as version management and change control in the software development process. In the process of software development and configuration, there will always be many problems that are difficult to detect. For example, when inputting the program code, there are not always some letter or space errors, and these errors are difficult to detect in time. For this reason, we need to establish a monitoring and management system for software development. As a computing model of human brain neural network, the artificial neural network can play the role of monitoring and management when it is applied to software development and configuration, which provides support for the security and scientificity of software development and configuration systems. This study studies the role and effectiveness of an artificial neural network in the monitoring and management of software development and configuration and validates it through experiments. The experimental results show that the artificial neural network has a strong ability to identify the problems in the software development configuration, which can improve the software development efficiency by at least 20%. It can improve the quality of software development and then improve the life cycle of software.

Software Configuration Management (SCM) aims to control the development of complex software systems. Traditional SCM systems treat text files as central artifacts, so they are mainly developed for source code. Such a system is not suitable for model-based software development with model-centric artifacts. When applying traditional systems to model-based software development, new challenges such as model mapping, differentiation, and merging arise. Many existing methods mainly use UML or domain-specific languages to determine model differences. However, as far as we know, there is no such technology for System Modeling Language (SysML) models. SysML covers the entire development life cycle of various complex systems, covering information, processes, hardware and software. SysML contains nine types of diagrams for system modeling. One of them is the SysML requirement diagram, which is used to capture the functional requirements of the system. We propose a differentiation method for the SysML demand model. We recommend to create a SysML requirement model in the CASE tool first, and then export the SysML model in the form of XMI. Then, we parse the XMI representation through difference calculations. Finally, we summarize the results in annotated form. We implemented our method in a satellite system case study and demonstrated the experimental use of the method.

Late product delivery is a common problem in software projects. Late delivery can be attributed to software change especially in the development and maintenance phases, leading to more work than originally planned, thus increasing project completion time. One approach for managing software change is Software Configuration Management (SCM). SCM was first introduced in the 1970s to ensure the timely delivery of software products. To date, SCM implementation is supported by recognized standards, best practice, and countless commercial and proprietary tools. However, after more than 50 years, the issue of late software product delivery still prevails, questioning the practice of SCM in software organizations. One aspect of SCM that has received little focus in mainstream research is the human aspects. This study aims to identify how the competency of SCM practitioners can be assessed through the identification of SCM competency criteria and the development of a competency assessment framework. The framework was validated through expert reviews and case studies involving practitioners from the public sector, industry, institutes of higher learning and international organizations. Results confirmed the reliability of the SCM competency criteria and the plausibility of the competency assessment framework.

Development of large-scale healthcare software projects essentially need the efficient management of the created software artifacts during software development process. In such projects different versions of an artifact are created at different times. Traditional software configuration management systems, such as Git, Subversion (SVN), etc., are designed for later phases of software development, which mainly handle the source code document. These systems are unable to perform difference detection and version management tasks on models such as unified modeling language diagrams. UML use case model is used for capturing functional requirements at analyses phase. Different versions of the use case model are created during analyses phase. This paper addresses the detection of differences between two versions of a use case model. In order to perform difference detection, we need to perform three main tasks, i. e., extract the contents of the model, comparison of models and difference representation. Most of the existing approaches in literature of model comparison deal with UML class diagrams. To the best of our knowledge, so far no appropriate approach addresses difference computation of use case model. Existing approaches are not applicable on use case model due to different semantics of use case model. In this research, the concept of model-based software configuration management (SCM) for use case difference detection is proposed. The use case models are created in an open source tool, starUML. The proposed difference algorithm is applied on intermediate tree structure representation of models. As a case study, different versions of a patient appointment healthcare system is used to evaluate different evaluation parameters, such as accuracy, domain independence, high conceptual level and tool independence.

Development of large-scale healthcare software projects essentially need the efficient management of the created software artifacts during software development process. In such projects different versions of an artifact are created at different times. Traditional software configuration management systems, such as Git, Subversion (SVN), etc., are designed for later phases of software development, which mainly handle the source code document. These systems are unable to perform difference detection and version management tasks on models such as unified modeling language diagrams. UML use case model is used for capturing functional requirements at analyses phase. Different versions of the use case model are created during analyses phase. This paper addresses the detection of differences between two versions of a use case model. In order to perform difference detection, we need to perform three main tasks, i. e., extract the contents of the model, comparison of models and difference representation. Most of the existing approaches in literature of model comparison deal with UML class diagrams. To the best of our knowledge, so far no appropriate approach addresses difference computation of use case model. Existing approaches are not applicable on use case model due to different semantics of use case model. In this research, the concept of model-based software configuration management (SCM) for use case difference detection is proposed. The use case models are created in an open source tool, starUML. The proposed difference algorithm is applied on intermediate tree structure representation of models. As a case study, different versions of a patient appointment healthcare system is used to evaluate different evaluation parameters, such as accuracy, domain independence, high conceptual level and tool independence.

Human Competency Considerations in an Integrated Software Configuration Management Framework

Software Configuration Management (SCM) is a process that supports Software Development Life Cycle (SDLC), which benefits project management, development activities, maintenance activities, quality assurance activities, and the end users of the software product. However, findings from series of interviews have proven that SCM is difficult to operate and to be implemented by the practitioners in public universities specifically in Malaysia. Some of the SCM activities could be aided by using the SCM automated tools, however not all practitioners opted that due to inflexibility of the tools. Theoretical and empirical study have identified and revealed the current practices, success factors, essential elements, and components of SCM that were used frequently and beneficial among software practitioners. The empirical study also found that the main components of s-SCMM are process, SCM tool, and human. The s-SCMM was proposed and developed by considering the main components: process, tools, human, and success factors. The s-SCMM model was validated through expert review. As the result, a Simplified SCM Model (or s-SCMM) was proposed and developed to help the execution and implementation of SCM in public university environment. The s-SCMM model is simplified by focusing on change management, version control, system building, and release management. Therefore, s-SCMM will become a platform to assist total implementation of SCM in organization. By using this model, the preparation and usage of SCM artefacts could become more effective, efficient, and systematic.

Context : In any discipline, replications of empirical studies are necessary to consolidate the acquired knowledge. In Software Engineering, replications have been reported since the 1990s, although their number is still small. The difficulty in publishing, the lack of guidelines, and the unavailability of replication packages are pointed out by the community as some of the main causes. Objective : Understanding the current state of replications in Software Engineering studies by evaluating current trends and evolution during the last 6 years. Method : A Systematic Mapping Study including articles published in the 2013–2018 period that report at least one replication of an empirical study in Software Engineering. Results : 137 studies were selected and analysed, identifying: ${i}$ ) forums; ii) authors, co-authorships and institutions; iii) most cited studies; iv) research topics addressed; ${v}$ ) empirical methods used; vi) temporal distribution of publications; and vii) distribution of studies according to research topics and empirical methods. Conclusions : According to our results, the most relevant forums are the Empirical Software Engineering and Information and Software Technology journals, and the Empirical Software Engineering and Measurement conference. We observed that, as in previous reviews by other researchers, most of the studies were carried out by European institutions, especially Italian, Spanish, and German researchers and institutions. The studies attracting more citations were published mainly in journals and in the International Conference on Software Engineering . Testing, requirements, and software construction were the most frequent topics of replication studies, whereas the usual empirical method was the controlled experiment. On the other hand, we identified research gaps in areas such as software engineering process, software configuration management, and software engineering economics. When analysed together with previous reviews, there is a clear increasing trend in the number of published replications in the 2013–2018 period.

Context:Knowledge level and productivity of the software engineering (SE) workforce are the subject of regular discussions among practitioners, educators, and researchers. There have been many efforts to measure and improve the knowledge gap between SE education and industrial needs.Objective:Although the existing efforts for aligning SE education and industrial needs have provided valuable insights, there is a need for analyzing the SE topics in a more “fine-grained” manner; i.e., knowing that SE university graduates should know more about requirements engineering is important, but it is more valuable to know the exact topics of requirements engineering that are most important in the industry.Method:We achieve the above objective by assessing the knowledge gaps of software engineers by designing and executing an opinion survey on levels of knowledge learned in universities versus skills needed in industry. We designed the survey by using the SE knowledge areas (KAs) from the latest version of the Software Engineering Body of Knowledge (SWEBOK v3), which classifies the SE knowledge into 12 KAs, which are themselves broken down into 67 subareas (sub-KAs) in total. Our analysis is based on (opinion) data gathered from 129 practitioners, who are mostly based in Turkey.Results:Based on our findings, we recommend that educators should include more materials on software maintenance, software configuration management, and testing in their SE curriculum. Based on the literature as well as the current trends in industry, we provide actionable suggestions to improve SE curriculum to decrease the knowledge gap.

The abundance of event data in current software configuration management systems makes it possible to discover software process models automatically by using actual observed behavior. However, traditional process mining algorithms cannot be applied to event logs recorded in software configuration management (SCM) systems, such as SVN, because of missing activity attributes. To address this problem, a software process activity classifier is proposed to build event-activity mapping relationships from software development event streams, revealing activity attributes and associating the activity to the original SVN log. The proposed approach extracts activity from the SVN log based on semantic features and introduces a novel technique based on a naive Bayes approach to associate event activities dynamically. The approach has been applied to two real-world software development process logs, ArgoUML and jEdit , consisting of more than 80,000 events, covering development information from 1998 to 2015. With the application of our approach to such data, activities can be extracted from event logs and a classifier can be constructed for adding activity attributes to new events. The results of the classification are evaluated in terms of precision rate , recall rate, and the F-measure . Overall, two real-world software development process logs are used to validate the method, and the experimental results show that the approach can mine software process activities from SVN log events automatically and in real-time.

Landscaping systematic mapping studies in software engineering: A tertiary study