Software Engineering Practices Research Articles

Logging Practices in Software Engineering: A Systematic Mapping Study

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</i> Logging practices provide the ability to record valuable runtime information of software systems to support operations tasks such as service monitoring and troubleshooting. However, current logging practices face common challenges. On the one hand, although the importance of logging practices has been broadly recognized, most of them are still conducted in an arbitrary or ad-hoc manner, ending up with questionable or inadequate support to perform these tasks. On the other hand, considerable research effort has been carried out on logging practices, however, few of the proposed techniques or methods have been widely adopted in industry. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> This study aims to establish a comprehensive understanding of the research state of logging practices, with a focus on unveiling possible problems and gaps which further shed light on the potential future research directions. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method:</i> We carried out a systematic mapping study on logging practices with 56 primary studies. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> This study provides a holistic report of the existing research on logging practices by systematically synthesizing and analyzing the focus and inter-relationship of the existing research in terms of issues, research topics and solution approaches. Using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3W1H</i> — <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>W</b>hy to log</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>W</b>here to log</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>W</b>hat to log</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>H</b>ow well is the logging</i> —as the categorization standard, we find that: (1) the best known issues in logging practices have been repeatedly investigated; (2) the issues are often studied separately without considering their intricate relationships; (3) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Where and What</i> questions have attracted the majority of research attention while little research effort has been made on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Why</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">How well</i> questions; and (4) the relationships between issues, research topics, and approaches regarding logging practices appear many-to-many, which indicates a lack of profound understanding of the issues in practice and how they should be appropriately tackled. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusions:</i> This study indicates a need to advance the state of research on logging practices. For example, more research effort should be invested on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">why to log</i> to set the anchor of logging practices as well as on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">how well is the logging</i> to close the loop. In addition, a holistic process perspective should be taken into account in both the research and the adoption related to logging practices.

Quality Evaluation of Modern Code Reviews Through Intelligent Biometric Program Comprehension

Code review is an essential practice in software engineering to spot code defects in the early stages of software development. Modern code reviews (e.g., acceptance or rejection of pull requests with Git) have become less formal than classic Fagan's inspections, lightweight, and more reliant on individuals (i.e., reviewers). However, reviewers may encounter mentally demanding challenges during the code review, such as code comprehension difficulties or distractions that might affect the code review quality. This work proposes a novel approach that evaluates the quality of code reviews in terms of bug-finding effectiveness and provides the reviewers with a clear message of whether the review should be repeated, indicating the code regions that may not have been well-reviewed. The proposed approach utilizes biometric information collected from the reviewer during the review process using non-intrusive biofeedback devices (e.g., smartwatches). Biometric measures such as Heart Rate Variability (HRV) and task-evoked pupillary response are captured as a surrogate of the cognitive state of the reviewer (e.g., mental workload) and inexpensive desktop eye-trackers compatible with the software development settings. This work uses Artificial Intelligence techniques to predict the cognitive load from the extracted biomarkers and classify each code region according to a set of features. The final evaluation considers various factors such as code complexity, time of the code review, the experience level of the reviewer, and other factors. Our experimental results show the approach could predict the review quality with 87.77%±4.65 accuracy and a Spearman correlation coefficient of 0.85 (p-value < 0.001) between the predicted and the actual review performance. This evaluation validates the cognitive load measurement using electroencephalography (EEG) signals as ground truth for the HRV and pupil signals.

Artificial Intelligence and Machine Learning in Dialysis: Ready for Prime Time?

Artificial Intelligence and Machine Learning in Dialysis: Ready for Prime Time?

Blockchain technology and software engineering practices: a systematic review of literature using topic modelling approach

Blockchain technology and software engineering practices: a systematic review of literature using topic modelling approach

Value-based Software Engineering: A Systematic Mapping Study

Background: Integrating value-oriented perspectives into the principles and practices of software engineering is fundamental to ensure that software development activities address key stakeholders’ views and also balance short-and long-term goals. This is put forward in the discipline of value-based software engineering (VBSE) Aim: This study aims to provide an overview of VBSE with respect to the research efforts that have been put into VBSE. Method: We conducted a systematic mapping study to classify evidence on value definitions, studies’ quality, VBSE principles and practices, research topics, methods, types, contribution facets, and publication venues. Results: From 143 studies we found that the term “value” has not been clearly defined in many studies. VB Requirements Engineering and VB Planning and Control were the two principles mostly investigated, whereas VB Risk Management and VB People Management were the least researched. Most studies showed very good reporting and relevance quality, acceptable credibility, but poor in rigour. Main research topic was Software Requirements and case study research was the method used the most. The majority of studies contribute towards methods and processes, while very few studies have proposed metrics and tools. Conclusion: We highlighted the research gaps and implications for research and practice to support VBSE.

Model to improve an ERP implementation based on agile best practice: A Delphi study.

Enterprise Resource Planning (ERP) is a business system that supports most of the critical processes of a company. It helps maintain a unified and reliable repository of information for decision-making. Implementing an ERP is a complex project, which implies a high level of effort and investment. Although the methodologies provided by the leading ERP providers are beneficial, there is still a high failure rate in the implementation.Many authors have analyzed the factors and causes of these implementation failures. There is also research to propose new implementation models that replace the existing ones. However, there is little research on existing methodologies incorporating new concepts from other disciplines to improve them.In this research, we propose a model that complements the current methodologies and uses the best practices in project management and software engineering directly related to the issues found in the literature. We submitted it to a group of experts on the subject to validate the model based on the Delphi method.

On the Relationship Between Organizational Structure Patterns and Architecture in Agile Teams

Forming members of an organisation into coherent groups or teams is an important issue in any large-scale software engineering endeavour, especially so in agile software development where teams rely heavily on self-organisation and organisational flexibility. But <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">is there a recurrent organisational structure pattern in agile software engineering teams?</i> and if so <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">what does that pattern imply, in terms of software architecture quality?</i> We address these questions using mixed-methods research in industry featuring interviews, surveys, and Delphi studies of real agile teams. In our study of 30 agile software teams we found that, out of seven organisational structure patterns that recur across our dataset, a single organisational pattern occurs over 37% of the time. This pattern: (a) reflects young communities (1-12 months old); (b) disappears in established ones (13+ months); and (c) reflects the highest number of architecture smells reported. Finally, we observe a negative correlation between a proposed organisational measure and architecture smells. On the one hand, these insights may serve to aid architects in designing not only their architectures but also their communities to best support their co-evolution. On the other hand, we observe that organisational structures in software engineering influence much more than simply software architectures, and we expect our results to lay the foundations of more structured and rigorous approaches to organisational structure studies and use in software engineering research and practice.

A methodology of automatic class diagrams generation from source code using Model-Driven Architecture and Machine Learning to achieve Energy Efficiency

The automated generation of class diagrams is a crucial task in software engineering, facilitating the understanding, analysis, and documentation of complex software systems. Traditional manual approaches are time and energy consuming, error-prone, and lack consistency. To address these challenges, this research presents an automated proposed approach that utilizes Graph Neural Networks (GNNs), a machine learning algorithm, to generate class diagrams from source code within the context of Model Driven Architecture (MDA) and reverse engineering. A comprehensive case study is conducted to compare the results obtained from the automated approach with manually created class diagrams. The GNN model demonstrates high accuracy in capturing the system’s structure, associations, and relationships. Notably, the automated approach significantly reduces the time required for class diagram generation, leading to substantial time and energy savings. By advancing automated software documentation, this research contributes to more efficient software engineering practices. It promotes consistency, eliminates human errors, and enables software engineers to focus on higher-value tasks. Overall, the proposed approach showcases the potential of GNNs in automating class diagram generation and its practical benefits for software development and documentation.

Pipeline Infrastructure Required to Meet the Requirements on AI

The theme of this issue is infrastructure as code (IaC). This concept typically refers to the application of software engineering practices in managing deployment infrastructure. Moving from physical hardware to virtual machines using configuration files that are way more flexible—and they can be version controlled and distributed. As such, IaC paves the way for automation and DevOps. This column touches upon similar topics: pipeline infrastructure to meet regulatory requirements on trustworthy AI solutions.

Asset Management in Machine Learning: State-of-research and State-of-practice

Machine learning components are essential for today’s software systems, causing a need to adapt traditional software engineering practices when developing machine-learning-based systems. This need is pronounced due to many development-related challenges of machine learning components such as asset, experiment, and dependency management. Recently, many asset management tools addressing these challenges have become available. It is essential to understand the support such tools offer to facilitate research and practice on building new management tools with native supports for machine learning and software engineering assets. This article positions machine learning asset management as a discipline that provides improved methods and tools for performing operations on machine learning assets. We present a feature-based survey of 18 state-of-practice and 12 state-of-research tools supporting machine-learning asset management. We overview their features for managing the types of assets used in machine learning experiments. Most state-of-research tools focus on tracking, exploring, and retrieving assets to address development concerns such as reproducibility, while the state-of-practice tools also offer collaboration and workflow-execution-related operations. In addition, assets are primarily tracked intrusively from the source code through APIs and managed via web dashboards or command-line interfaces (CLIs). We identify asynchronous collaboration and asset reusability as directions for new tools and techniques.

Software assistants in software engineering: A systematic mapping study

AbstractThe increasing essential complexity of software systems makes current software engineering methods and practices fall short in many occasions. Software assistants have the ability to help humans achieve a variety of tasks, including the development of software. Such assistants, which show human‐like competences such as autonomy and intelligence, help software engineers do their job by empowering them with new knowledge. This article investigates the research efforts that have been conducted on the creation of assistants for software design, construction and maintenance paying special attention to the user‐assistant interactions. To this end, we followed the standard systematic mapping study method to identify and classify relevant works in the state of the art. Out of the 7580 articles resulting from the automatic search, we identified 112 primary studies that present works which qualify as software assistants. We provide all the resources needed to reproduce our study. We report on the trends and goals of the assistants, the tasks they perform, how they interact with users, the technologies and mechanisms they exploit to embed intelligence and provide knowledge, and their level of automation. We propose a classification of software assistants based on interactions and present an analysis of the different automation patterns. As outcomes of our study, we provide a classification of software assistants dealing with the design, construction and maintenance phases of software development, we discuss the results, identify open lines of work and challenges and call for new innovative and rigorous research efforts in this field.

Engineering Blockchain-based Software Systems: Foundations, Survey, and Future Directions

Many scientific and practical areas have shown increasing interest in reaping the benefits of blockchain technology to empower software systems. However, the unique characteristics and requirements associated with Blockchain-based Software (BBS) systems raise new challenges across the development lifecycle that entail an extensive improvement of conventional software engineering. This article presents a systematic literature review of the state-of-the-art in BBS engineering research from the perspective of the software engineering discipline. We characterize BBS engineering based on the key aspects of theoretical foundations, processes, models , and roles . Based on these aspects, we present a rich repertoire of development tasks, design principles, models, roles, challenges, and resolution techniques. The focus and depth of this survey not only give software engineering practitioners and researchers a consolidated body of knowledge about current BBS development but also underpin a starting point for further research in this field.

Towards the construction of computational models of emotions from the perspective of a software system

Computational models of emotion (CMEs) are software systems designed to emulate the diverse aspects of the human emotion process. This type of model is commonly incorporated into cognitive agent architectures to provide mechanisms underlying affective behavior. The construction of CMEs involve theories that explain human emotion as well as computational artifacts related to the design and implementation of software systems. Although most CMEs reported in the literature provide details on their theoretical foundations, it is uncommon to find details about the computational practices and artifacts utilized during their design and implementation phases. This paper presents and discusses some challenges associated with the computational nature of this type of model: (i) Software quality attributes in CMEs, (ii) Interoperability between CMEs and cognitive components, (iii) Formal procedures for the design of CMEs, and (iv) Reference schemes to validate CMEs. Software engineering is used as a reference to propose and discuss these challenges. In addition, a reference architecture designed by following software engineering practices and artifacts is proposed to discuss the implications of addressing these challenges. The present research is at the intersection of human emotion modeling and software engineering to contribute to the software development process of affective systems.

On business adoption and use of reproducible builds for open and closed source software

Reproducible builds (R-Bs) are software engineering practices that reliably create bit-for-bit identical binary executable files from specified source code. R-Bs are applied in some open source software (OSS) projects and distributions to allow verification that the distributed binary has been built from the released source code. The use of R-Bs has been advocated in software maintenance and R-Bs are applied in the development of some OSS security applications. Nonetheless, industry application of R-Bs appears limited, and we seek to understand whether awareness is low or if significant technical and business reasons prevent wider adoption. Through interviews with software practitioners and business managers, this study explores the utility of applying R-Bs in businesses in the primary and secondary software sectors and the business and technical reasons supporting their adoption. We find businesses use R-Bs in the safety-critical and security domains, and R-Bs are valuable for traceability and support collaborative software development. We also found that R-Bs are valued as engineering processes and are seen as a badge of software quality, but without a tangible value proposition. There are good engineering reasons to use R-Bs in industrial software development, and the principle of establishing correspondence between source code and binary offers opportunities for the development of further applications.

Exception Handling as a Social Concern

Exception handling has been successfully proposed in software engineering practice as a simple but effective technology to address abnormal situations possibly occurring at runtime. In this article, we present and explain the advantages of how exception handling can be seamlessly integrated in distributed systems—which are realized as multiagent systems—at an organizational level, by relying on the notion of responsibility.

Software Engineering Practices for Machine Learning — Adoption, Effects, and Team Assessment

Software Engineering Practices for Machine Learning — Adoption, Effects, and Team Assessment

Software Engineering Practices for Machine Learning — Adoption, Effects, and Team Assessment

Software Engineering Practices for Machine Learning — Adoption, Effects, and Team Assessment

The journey to technical excellence in agile software development

Technical excellence is a nebulous term in agile software development. This vagueness is risky because it may lead to misunderstandings and to agile implementations that may overlook a key principle of agile development. This study investigates how agile practitioners interpret the concept of technical excellence brought up in Principle 9 of the Agile manifesto. Moreover, we investigate how agile practitioners put the concept into practice and what conditions facilitate putting technical excellence into practice. We conducted semi-structured interviews with twenty agile practitioners, coded the data inductively, and performed two sessions to validate the emerging findings. We find that technical excellence is first and foremost a mindset that is underpinned by continuous attention to sustainable code, continuous learning, and teamwork. Fostering technical excellence requires the adoption of design and development practices, such as continuous architecting, and is supported by continuous learning. We also identify three enabling conditions for technical excellence: Leadership support, customer buy-in, and psychological safety. These enablers provide teams with leeway to nurture their pursuit of technical excellence. Our findings highlight the key role of people-based strategies in promoting technical excellence in agile software development. They show that the attainment of technical excellence does not only involve technical practices. On the contrary, it relies on social and organizational support and, most importantly, a mindset. • Technical excellence is the only reference to quality in the agile manifesto • This study investigated the interpretation and implementation of technical excellence • Technical excellence is a mindset supported by software engineering practices • Technical excellence does not happen without the team members’ commitment • Technical excellence is nurtured by various social conditions

Software Engineering After the COVID-19 Outbreak

Software Engineering (SE) has evolved over many decades and has led to many proven and well-established methods and tools that support the efficient development of software and IT systems in general. Although software development had often been performed by distributed teams even before the pandemic, the COVID-19 outbreak exacerbated the physical separation of teams, creating numerous challenges and issues for both organizations and developers. Therefore, in this new column "Software Engineering After the COVID-19 Outbreak", we aim at providing insights into the impact of the COVID-19 outbreak on the work of Software Engineering practitioners and researchers. We intend to collect past and current challenges, changes in structures and organizations, reports of experiences and, of course, proven solutions for solving problems that have arisen.

The evolution of the code during review: an investigation on review changes

Code review is a software engineering practice in which reviewers manually inspect the code written by a fellow developer and propose any change that is deemed necessary or useful. The main goal of code review is to improve the quality of the code under review. Despite the widespread use of code review, only a few studies focused on the investigation of its outcomes, for example, investigating the code changes that happen to the code under review. The goal of this paper is to expand our knowledge on the outcome of code review while re-evaluating results from previous work. To this aim, we analyze changes that happened during the review process, which we define as review changes. Considering three popular open-source software projects, we investigate the types of review changes (based on existing taxonomies) and what triggers them; also, we study which code factors in a code review are most related to the number of review changes. Our results show that the majority of changes relate to evolvability concerns, with a strong prevalence of documentation and structure changes at type-level. Furthermore, differently from past work, we found that the majority of review changes are not triggered by reviewers’ comments. Finally, we find that the number of review changes in a code review is related to the size of the initial patch as well as the new lines of code that it adds. However, other factors, such as lines deleted or the author of the review patchset, do not always show an empirically supported relationship with the number of changes.