Software Engineering Research Articles

Editorial para el número especial de la 12a Conferencia Internacional sobre Investigación e Innovación en Ingeniería de Software

Software Engineering is responsible for analyzing, designing, implementing, testing and maintaining software developments. It is currently a very important area since software is found everywhere in our lives, we are immersed in technological advances. Given the importance of Software Engineering, this special issue presents the research articles in Spanish from the 12th International Conference on Software Engineering Research and Innovation.

Exploring the synergy between generative AI and software engineering: Automating code optimization and bug fixing

As applied to software engineering, generative AI is quickly transitioning from a zero-sum industry game changer into the primary automation tool for code optimization, bug identification, and problem-solving. This technology takes advantage of artificial intelligence algorithms within machine learning models to analyze and write code, resulting in improved quality and speed of an application development process. The generative AI replenishes productivity in development work and enhances centralization between development work teams through code handling and intelligent suggestions for essential codes. However, the integration of AI in software engineering poses the following problems and ethical questions: the question of accuracy, bias, and data. This paper will review the existing knowledge on generative AI in software engineering regarding its current use, future evolutions and advancements, issues and limitations, and ethical factors in using this technology. This paper considers these aspects to give a global outlook on how generative AI will transform software development in the future and how responsible AI should be employed.

Integrating Generative AI into the Software Development Lifecycle: Impacts on Code Quality and Maintenance

Recent advances in generative AI have depicted it as a revolutionary approach in the software development technologies pioneered to improve the codes' reliability and sustain their quality and performance. Generative AI tools can help develop code independently, suggest intelligent solutions and ideas, and enhance several development procedures thanks to superior algorithms and machine learning features. This paper discusses how generative AI can/has been applied within software development to achieve the following three goals: First, to increase the code quality using automated code generation/review. Second, the code maintainability should be improved through standards and documentation. Third, to increase the up-to-speed development productivity due to AI-based automation, namely the automation of repetitive tasks and fast prototyping. The paper also considers issues and difficulties that can be tied to AI in this context: problems of dependence on AI, ethical and security issues, and technical imperfections. Finally, the implications of generative AI in software development in the future are presented, which can open a new direction in the development of software products while primarily pointing to the processes of managing the introduction of generative AI. Using the evaluation of the current possibilities and future perspectives of generative AI presented in this paper, one can conclude its impact on the future of software engineering.

Enhancing software engineering practices with generative AI: A framework for automated code synthesis and refactoring

This paper is based on how software development has been revolutionized using AI in automation, mainly dealing with code synthesis and rewrite frameworks. While there is no focused definition for software development with AI technologies, their application in development processes is unlikely to remain marginal as they mature and provide higher productivity, improved code quality, and enhanced ability for automating repetitive tasks in development. Automating coding means that predefined tools can bring code suggestions, show and apply refactoring features, and enforce coding standards so developers can devote their efforts to other aspects of software development. The following paper describes the practical systems that should be adopted before AI can be incorporated into software development. It also covers essential factors such as ethical, security, and quality aspects regarding the proper use of artificial intelligence. Lastly, the paper focuses on the direction and innovations in the future, including domain-specific AI models, better explainability of the AI solutions, and collaborative tools that can work according to improved and modified development practices. The framework is intended to balance machine learning with human experience to help developers utilize the benefits that an AI-Based Software Development Framework can render.

Systems engineering barriers to legacy system evolution: Legacy system assessment

AbstractThis study evaluates the effectiveness of common engineering processes in conducting system assessments, with a focus on legacy system assessments, which are currently a barrier to legacy system evolution. These processes include reverse systems engineering (RSE), which remains in its infancy, and more established processes such as software and hardware reverse engineering. The study assesses these methods to better understand their ability to define systems of interest and meet the criteria set by the four horsemen of traditional systems engineering, as defined by Rebovich and White, and the International Council of Systems Engineering. Additionally, RE techniques are reviewed to ensure that a formal, systems‐level process—with detailed steps and diagrams—is documented and accessible to systems or reverse engineers. Lastly, this investigation elucidates the ability of each reverse engineering process to discover, recover, or reproduce information pertaining to the systems of interest that is missing, incomplete, or outdated in documentation relevant to the development or operations of that system. This information can be used to better understand the areas of RSE that still need to be developed and aid in the maturation of a repeatable process that is applicable to legacy systems. The results of this study are expected to contribute to the further development of the Enterprise Lifecycle Model proposed by Terry and Chandrasekar.

Models2Code: Autonomous model‐based generation to expedite the engineering process

AbstractInsufficient resources and high costs are hindering industrial development, potentially impeding adaptation to market demands. Overcoming this challenge necessitates advancements in software engineering techniques to streamline processes and meet industrial requirements. Crucially, automating manual tasks and enhancing interoperability between engineering stages can yield efficiency gains. This paper presents a model‐based system engineering approach aimed at automating the transition from design to implementation, incorporating autonomous generation and validation features. Implemented as plugins and utilizing model transformation techniques, this solution targets reducing engineering time and facilitating the adoption of new technologies. Developed, implemented, and tested within the Arrowhead framework, the approach is followed by a discussion on its benefits and limitations.

Memoization in Model Checking for Safety Properties with Multi-Swarm Particle Swarm Optimization

In software engineering, errors or faults in software systems often lead to critical social problems. One effective methodology to tackle this problem is model checking, which is an automated formal verification technique. In traditional model checking, the task of finding specification errors is reduced to deterministic search techniques such as Depth-First Search. Recent research has shown that swarm intelligence offers a powerful search capability compared to traditional techniques. In particular, multi-swarm Particle Swarm Optimization is known to be efficient and can mitigate the state-space explosion problem, i.e., the exponential increase in the search space with a linear increase in the problem size. However, the state-space explosion problem is still significant when verifying very large systems. Further performance improvement is needed. To achieve this, we propose a novel memoization or cache mechanism for storing tentative solutions for reuse in the later stages of the search procedure. For each stage, a candidate solution computed by a swarm is summarized efficiently and heuristically to consolidate similar solutions into a single representative solution. We store the summary and its associated solutions in key-value maps. Instead of computing known solutions repeatedly, we retrieve the solution if the stored key matches the summary. We incorporated the proposed mechanism into a model-checking technique with multi-swarm Particle Swarm Optimization and evaluated the search performance. We show in this paper that the proposed mechanism improved time and space consumption while maintaining solution quality.

Using rapid reviews to support software engineering practice: a systematic review and a replication study

Using rapid reviews to support software engineering practice: a systematic review and a replication study

Construction of the Curriculum System of Software Engineering Based on the Agile Development Method

Under the background of “new engineering” construction, software engineering teaching pays more attention to cultivating students’ engineering practice and innovation ability. In view of the inconsistency between development and demand design, team division of labor, difficult measurement of individual contribution, single assessment method, and other problems in traditional practice teaching, this paper proposes that under the guidance of agile development methods, software engineering courses should adopt Scrum framework to organize course project practice, use agile collaboration platform to implement individual work, follow up experiment progress, and ensure effective project advancement. The statistical data of curriculum “diversity” assessment show that there is an obvious improvement effect on students’ software engineering ability and quality.

Accelerating Evidence Synthesis in Observational Studies: Development of a Living Natural Language Processing-Assisted Intelligent Systematic Literature Review System.

Systematic literature review (SLR), a robust method to identify and summarize evidence from published sources, is considered to be a complex, time-consuming, labor-intensive, and expensive task. This study aimed to present a solution based on natural language processing (NLP) that accelerates and streamlines the SLR process for observational studies using real-world data. We followed an agile software development and iterative software engineering methodology to build a customized intelligent end-to-end living NLP-assisted solution for observational SLR tasks. Multiple machine learning-based NLP algorithms were adopted to automate article screening and data element extraction processes. The NLP prediction results can be further reviewed and verified by domain experts, following the human-in-the-loop design. The system integrates explainable articificial intelligence to provide evidence for NLP algorithms and add transparency to extracted literature data elements. The system was developed based on 3 existing SLR projects of observational studies, including the epidemiology studies of human papillomavirus-associated diseases, the disease burden of pneumococcal diseases, and cost-effectiveness studies on pneumococcal vaccines. Our Intelligent SLR Platform covers major SLR steps, including study protocol setting, literature retrieval, abstract screening, full-text screening, data element extraction from full-text articles, results summary, and data visualization. The NLP algorithms achieved accuracy scores of 0.86-0.90 on article screening tasks (framed as text classification tasks) and macroaverage F1 scores of 0.57-0.89 on data element extraction tasks (framed as named entity recognition tasks). Cutting-edge NLP algorithms expedite SLR for observational studies, thus allowing scientists to have more time to focus on the quality of data and the synthesis of evidence in observational studies. Aligning the living SLR concept, the system has the potential to update literature data and enable scientists to easily stay current with the literature related to observational studies prospectively and continuously.

Software codesign between end users and developers to enhance utility for biodiversity conservation

Abstract Creating software tools that address the needs of a wide range of decision-makers requires the inclusion of differing perspectives throughout the development process. Software tools for biodiversity conservation often fall short in this regard, partly because broad decision-maker needs may exceed the toolkits of single research groups or even institutions. We show that participatory, collaborative codesign enhances the utility of software tools for better decision-making in biodiversity conservation planning, as demonstrated by our experiences developing a set of integrated tools in Colombia. Specifically, we undertook an interdisciplinary, multi-institutional collaboration of ecological modelers, software engineers, and a diverse profile of potential end users, including decision-makers, conservation practitioners, and biodiversity experts. We leveraged and modified common paradigms of software production, including codesign and agile development, to facilitate collaboration through all stages (including conceptualization, development, testing, and feedback) to ensure the accessibility and applicability of the new tools to inform decision-making for biodiversity conservation planning.

Requirements Engineering at a Turning Point: Key Research Trends Shaping the Future of Systems and Software Integration

Requirements Engineering (RE) is experiencing a significant transformation that requires urgent attention and creativity from industry experts to address the challenges of future systems and software development. At the core of this evolution is Requirements Engineering (RE), an essential process that defines, records, and maintains the fundamental aspects of software and systems design. With the rapid increase in complexity and interconnectedness across different industries, the issues linked to requirements engineering are becoming more pressing, significantly affecting costs, timelines, and overall performance. This article outlines current research trends in requirements engineering, focusing on emerging challenges and future pathways. We investigate advancements in RE techniques, methodologies, and tools, assessing their effects on building complex systems. Furthermore, we emphasize the growing significance of stakeholder engagement and agile practices in shaping requirements, highlighting their contributions to improving collaboration and adaptability. By evaluating the present state of practice and pinpointing critical areas of research concern, this article offers a guide for future requirements engineering studies, enabling researchers and practitioners to address the changing needs of software and systems engineering.

A Context-Based Perspective on Frost Analysis in Reuse-Oriented Big Data-System Developments

The large amount of available data, generated every second via sensors, social networks, organizations, and so on, has generated new lines of research that involve novel methods, techniques, resources, and/or technologies. The development of big data systems (BDSs) can be approached from different perspectives, all of them useful, depending on the objectives pursued. In particular, in this work, we address BDSs in the area of software engineering, contributing to the generation of novel methodologies and techniques for software reuse. In this article, we propose a methodology to develop reusable BDSs by mirroring activities from software product line engineering. This means that the process of building BDSs is approached by analyzing the variety of domain features and modeling them as a family of related assets. The contextual perspective of the proposal, along with its supporting tool, is introduced through a case study in the agrometeorology domain. The characterization of variables for frost analysis exemplifies the importance of identifying variety, as well as the possibility of reusing previous analyses adjusted to the profile of each case. In addition to showing interesting findings from the case, we also exemplify our concept of context variety, which is a core element in modeling reusable BDSs.

Using Music Features for Managing Revisions and Variants of Musical Scores

Abstract Music engravers nowadays use music notation software to create scores for musical works. As is common in any creative process, many different versions of digital artifacts are created—e.g., to manage editions of the same musical work, editorial markups, or the history and genesis of compositions. In the field of software engineering, researchers have proposed the use of features, i.e., user-visible aspects of systems, to manage both revisions and variants of source code and other software artifacts. Feature-based version control systems establish and maintain feature-to-code mappings defining which parts of the artifacts realize particular features, e.g., which code implements a specific function. These mappings can then be used to generate new variants based on the artifacts by selecting the desired features. Our article provides an in-depth study on feature-based version control for music notation. Our automated approach adopts the domain-specific language (DSL) LilyPond and the feature-oriented version control system ECCO. Existing studies show that features in musical scores are often fine-grained and affect only small parts of an artifact, are scattered across noncontiguous locations in the artifact, and highly interact with each other. Such properties have strong implications for the usefulness of versioning tools. Our experiment investigates two factors related to the correctness of output from feature-based version control systems when used for symbolic music notation. We demonstrate the incremental refinement of feature-to-artifact mappings when committing DSL code. We further study the impact of the order of feature interactions on the correctness of the automatically generated music artifacts. We find that a larger feature interaction threshold produces only marginally more correct results, but fixing and recommitting incorrect variants has a more powerful effect. Our results further show that considering DSL specifics is important for versioning fine-grained and scattered features.

BG-LoA: A Benchmarking Framework for BIM/GIS Data Integration Based on Meta-Modelling Theory

Abstract. There are various studies on the integration of building information modelling (BIM) and geographic information system (GIS) that convert BIM data into GIS data. These studies used different technologies, data standards, data models, and data formats such as RDF (Resource Description Framework), graph, CityGML, and Shapefile to address the data incompatibility problem between BIM and GIS. This makes it difficult to compare these studies for the purpose of assessing research innovation, and sometimes, improper comparisons were carried out between studies. To solve this problem, this study adopted the metamodeling theory used in model-driven software engineering (MDSE) and extended its 4-layer framework of level of abstraction. The new benchmarking framework, referred to as level of abstraction for BIM/GIS data integration (BG-LoA), has 5 levels, including meta metamodel (M3), metamodel (M2), model (M1), reality (data) (M0), and reality (real world) (M00). The common types of IFC (Industry Foundation Classes) conversion were analysed using the new framework, including IFC-to-CityGML, IFC-to-CityJSON, IFC-to-Shapefile, IFC-to-GML, IFC-to-3DTiles, IFC-to-RDF, IFC-to-IfcGraph, and IFC-to-UML. The result shows that the new framework can show the difference between those conversion types, which makes it easier for future studies to benchmark their work and assess level of contribution to the body of knowledge.

A survey on Cryptoagility and Agile Practices in the light of quantum resistance

Context:Crypto-agility, a name that stems from agile methodologies for software development, means the ability to modify quickly and securely cryptographic algorithms in the event of a compromise. The advent of quantum computing poses existential threats to current cryptography, having the power to breach current cryptography systems. Objective:We investigated whether and to what extent agile practices for software development are suited to support crypto-agility, or not. In particular, we discuss their usefulness in the context of substituting current algorithms with quantum-resistant ones. Method:First, we analyzed the literature to define a subset of 15 agile practices potentially relevant to cryptographic software development. Then, we developed a questionnaire to assess the suitability of agile practices for obtaining crypto-agility. We performed a Web search of relevant documents about crypto-agility and quantum resistance and sent their authors the questionnaire. We also sent the questionnaire to cybersecurity officers of four Italian firms. We analyzed and discussed the responses to 32 valid questionnaires. Results:The respondents’ affiliations are evenly distributed between researchers and developers. Most of them are active, or somehow active, in quantum-resistant cryptography and use agile methods. Most of the agile practices are deemed to be quite useful, or very useful to get crypto-agility, the most effective being Continuous Integration and Coding Standards; the least appreciated is Self-organizing Team. Conclusion:According to researchers and developers working in the field, the safe transition of cryptographic algorithms to quantum-resistant ones can benefit from the adoption of many agile practices. Further software engineering research is needed to integrate agile practices in more formal cryptographic software development processes.

FUNDAMENTANDO MEGAMODELOS: UMA REVISÃO SISTEMÁTICA

One approach that has gained strength in recent years in software engineering is the ModelDriven Engineering (MDE). The MDE is the most conceptual approach when working with models, and the MDA as putting in place the MDE. The MDE modeling paradigms can be used in simple designs with the use of models, but when working with relations between the models is the so-called megamodel. In other words, megamodel is a template that contains models and relationships between them. The objective of this study is to describe the results of a systematic review, whose objective was to know the state of the art megamodel and verify their use and the contexts in which they are applied. The results show that all selected works use megamodel in different contexts, but there is no consensus about its definition or nomenclature.

AI Copilot for the Modern Developer : Leveraging GenAI in Software Development

This paper explores the transformative impact of AI copilots on software development, powered by Generative AI and Large Language Models. It examines how these tools enhance developer productivity, improve code quality, and democratize software development. The study delves into the core technologies behind AI copilots, including code synthesis and contextual learning, and discusses their integration with development environments. The paper also investigates the role of reinforcement learning in continuously improving AI copilots and analyzes their impact on software development practices. Additionally, it explores future prospects, including more sophisticated natural language understanding and improved cross-language support. Throughout, the paper emphasizes how AI copilots are reshaping the landscape of software engineering, potentially leading to more efficient, higher-quality, and innovative software solutions.

A novel hybrid CNN-GRU model for website-based user interface code generator

As software engineering techniques evolve, conventional methods become less efficient due to prolonged development cycles and repetitive tasks. There is a growing need for automated systems that can incorporate User Interface (UI) designs into code. This study proposes a novel Hybrid CNN-GRU model for automatically generate website-based UI codes from UI design images. The proposed method consists of a visual model and a language-decoder part. The visual model employs pretrained CNN models, including EfficientNetV2B0, Xception, and EfficientNetV2M, to extract features from images, whereas a GRU serves as the language model and decoder. An empirical study was conducted to evaluate the effectiveness of various hybrid CNN-GRU approaches for generating UI codes. The EfficientNetV2B0 GRU model demonstrated the fastest training time, whereas the Xception GRU model was the most robust for various data complexities. The results of this study will advance the field of automated code generation, simplify the front-end development process, and shorten the time and effort necessitated for UI code implementations.

Influence of HRM practices on innovation in software engineering: the mediating role of developer experience

Influence of HRM practices on innovation in software engineering: the mediating role of developer experience