Information and Software Technology最新文献_第5页

Gated transformer network for multivariate security patch identification with mixture-of-experts 基于混合专家的门控变压器网络多变量安全补丁识别

IF 4.3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information and Software Technology

Pub Date : 2026-04-01 Epub Date: 2025-12-31 DOI: 10.1016/j.infsof.2025.108006

Jiajun Tong , Zhixiao Wang , Xiaobin Rui

Context:

Security patch identification is an important task in continuous integration and deployment, which helps software developers detect security issues and code vulnerabilities. Recent studies have confirmed that using both commit message and code diff information are beneficial to identification performance. However, existing works still face the problems of poor model representation ability and low model robustness, both of which affect the quality of commit representation, resulting in bad identification performance.

Objective:

We propose a gated transformer network for multivariate security patch identification with mixture-of-experts.

Method:

To improve the representation capability of the model and the quality of the commit representations, we provided a bi-encoder to utilize prior knowledge to enhance distinctive features for commit message and code diff respectively. To improve the robustness of the model and further improve the quality of commit representations, we designed a gated layer to learn the weight of each expert, and dynamically assign weights to different features.

Results:

Extensive experiments show that our framework has effectively improved the model representation ability, and the robustness of the model, providing high-quality commit representations, and achieves the state-of-the-art performance.

Conclusion:

Our approach provides a bi-encoder to obtain the embedding of each feature by two experts, and then explore the difference between them, by setting different weights through the gated layer. It not only improves the model representation ability but also improves the robustness of the model, thus having favorable applicability in real-world scenarios. The code and data are shared in https://github.com/AppleMax1992/ensemble_commit.

背景：安全补丁识别是持续集成和部署中的一项重要任务，它可以帮助软件开发人员发现安全问题和代码漏洞。最近的研究已经证实，同时使用提交消息和代码差异信息有助于提高识别性能。然而，现有的工作仍然面临着模型表示能力差和模型鲁棒性低等问题，这些问题都会影响提交表示的质量，导致识别性能不佳。目的：提出一种基于混合专家的多变量安全补丁识别的门控变压器网络。方法：为了提高模型的表示能力和提交表示的质量，我们提供了一个双编码器，利用先验知识分别增强提交消息和代码差异的显著特征。为了提高模型的鲁棒性和进一步提高提交表示的质量，我们设计了一个门控层来学习每个专家的权重，并动态地为不同的特征分配权重。结果：大量的实验表明，我们的框架有效地提高了模型的表示能力和模型的鲁棒性，提供了高质量的提交表示，达到了最先进的性能。结论：我们的方法提供了一个双编码器，通过两个专家获得每个特征的嵌入，然后通过门控层设置不同的权重来探索它们之间的差异。它不仅提高了模型的表示能力，而且提高了模型的鲁棒性，因此在实际场景中具有良好的适用性。代码和数据在https://github.com/AppleMax1992/ensemble_commit中共享。

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引用次数: 0

Consensus planning boosts LLM code generation 共识计划促进LLM代码生成

IF 4.3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information and Software Technology

Pub Date : 2026-04-01 Epub Date: 2026-01-12 DOI: 10.1016/j.infsof.2026.108030

Chao Wen , Jie Liu , Liang Du

While large language models (LLMs) have demonstrated impressive ability in natural language processing (NLP), they are struggling for addressing the code generation tasks with complicated human intent. It is universally recognized that humans require insights into problem descriptions, elaborate plans from collaborative perspectives and consciously organize modules prior to coding implementation. To achieve this aim, we introduce consensus to boost multi-agent prompting approach to code generation tasks by imitating human developers. The insights into consensus among distinct candidate plans are leveraged by LLM agent for mitigating discrepancies. The discrepancies indicate overlooked crucial details that may lead to potential errors. Besides, the consensus plan is exploited to firstly construct code modules at distinct levels and then hierarchically organize them for final code generation. We conduct extensive experiments on eight program synthesis benchmarks, three of which are challenging problem-solving. Experimental results show that the proposed framework showcases the improved reflection on code generation, achieving new state-of-the-art (pass@1) results. Moreover, our approach consistently delivers superior performance across various programming languages and varying problem difficulties. Code available at https://github.com/AISP-group/CPCG.

虽然大型语言模型（llm）在自然语言处理（NLP）方面表现出了令人印象深刻的能力，但它们在处理具有复杂人类意图的代码生成任务方面仍处于挣扎状态。人们普遍认为，在编码实现之前，人类需要洞察问题描述，从协作的角度制定详细计划，并有意识地组织模块。为了实现这一目标，我们引入共识，通过模仿人类开发人员来促进多智能体提示方法的代码生成任务。LLM代理利用对不同候选计划之间共识的洞察来减少差异。这些差异表明忽视了可能导致潜在错误的关键细节。此外，利用共识计划首先构建不同层次的代码模块，然后将它们分层组织以最终生成代码。我们在八个程序合成基准上进行了广泛的实验，其中三个是具有挑战性的问题解决。实验结果表明，该框架在代码生成方面表现出改进的反射，获得了新的最先进的结果（pass@1）。此外，我们的方法在各种编程语言和各种问题困难中始终提供卓越的性能。代码可从https://github.com/AISP-group/CPCG获得。

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引用次数: 0

Empirical analysis of generative AI tool adoption in software development 生成式人工智能工具在软件开发中的应用实证分析

IF 4.3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information and Software Technology

Pub Date : 2026-04-01 Epub Date: 2026-01-13 DOI: 10.1016/j.infsof.2026.108036

Deo Shao, Fredrick Ishengoma

Context

Software development is evolving with the emergence of Generative AI (GAI) tools that boost productivity, reduce manual errors, and accelerate workflows. However, little is known about how users perceive the usability, effectiveness, and security of these tools, especially among varied user populations.

Objectives

This study examines the determinants of GAI tool adoption. Specifically, it examines the behavioural determinants driving GAI adoption in software development and investigates how students compare with professionals in their perception of GAI adoption.

Methods

This study employs a cross-sectional, quantitative approach, comprising structured surveys distributed to software engineering students and senior engineers. The survey was designed based on the UTAUT framework. Data was collected from 305 participants (125 students, 133 professional developers, and 47 other tech professionals; industry total = 180). Descriptive statistics, t-tests, and regression analysis were conducted to analyse data and report trends and predictors of adoption intention.

Results

Social influence was the most important predictor of adoption intention (β = 0.945, p< 0.001), and its effect differed between groups. Compared to professionals, students are more cautious about security, though their responses are less technically specific. Professional developers employ systematic refinement strategies; a large percentage make extensive code changes to improve maintainability and ensure architectural alignment. By contrast, students exhibit different usage behaviour, focusing more on getting the final product working but less on code refinement and security issues.

Conclusion

This study fills the empirical gap in the diffusion of generative AI into software development. The findings suggest different patterns between students and professional developers. The results are of interest to educators, developers, and industry leaders. Future studies should examine adoption trends among a broader range of user groups and assess the long-term effects of GAI tools on software engineering.

随着生成式人工智能（GAI）工具的出现，软件开发正在不断发展，这些工具提高了生产力，减少了人工错误，并加快了工作流程。然而，对于用户如何看待这些工具的可用性、有效性和安全性，特别是在不同的用户群体中，我们所知甚少。目的本研究探讨GAI工具采用的决定因素。具体来说，它考察了在软件开发中推动GAI采用的行为决定因素，并调查了学生与专业人员在GAI采用的看法方面的比较。方法本研究采用横断面定量方法，包括结构化调查，分发给软件工程学生和高级工程师。该调查是根据UTAUT框架设计的。数据收集自305名参与者（125名学生，133名专业开发人员和47名其他技术专业人员；行业总数= 180）。采用描述性统计、t检验和回归分析来分析数据并报告采用意向的趋势和预测因素。结果社会影响是影响收养意向的最重要预测因子（β = 0.945, p< 0.001），且其影响在组间存在差异。与专业人士相比，学生在安全问题上更加谨慎，尽管他们的回答在技术上不那么具体。专业开发人员采用系统化的细化策略；很大一部分人进行了大量的代码更改，以提高可维护性并确保体系结构的一致性。相比之下，学生表现出不同的使用行为，他们更多地关注最终产品的工作，而较少关注代码的改进和安全问题。结论本研究填补了生成式人工智能在软件开发中推广的经验空白。研究结果表明，学生和专业开发人员之间存在不同的模式。其结果引起了教育工作者、开发人员和行业领导者的兴趣。未来的研究应该在更广泛的用户群体中检查采用趋势，并评估GAI工具对软件工程的长期影响。

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引用次数: 0

Automatic multi-language analysis of SOLID compliance via machine learning algorithms 通过机器学习算法对SOLID遵从性进行自动多语言分析

IF 4.3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information and Software Technology

Pub Date : 2026-04-01 Epub Date: 2026-01-05 DOI: 10.1016/j.infsof.2026.108013

Caner Balim , Naim Karasekreter , Özkan Aslan

Context

The SOLID design principles are fundamental in object-oriented software development, promoting modularity, maintainability, and scalability. Manual verification of these principles in code is often time-consuming and error-prone, especially in large-scale, multilingual projects. Since adherence to SOLID principles is closely linked to software quality, automating this verification can significantly enhance code reliability.

Objectives

This study proposes a machine learning-based approach for the automatic classification of SOLID principle compliance in object-oriented code. Specifically, we investigate the effectiveness of embedding representations generated by three pretrained transformer models: LongCoder and StarCoder2, which are both code-oriented, and BigBird, a general-purpose model, in supporting principle-specific classification across Java and Python codebases.

Methods

We compiled a novel multi-label dataset consisting of 1103 real-world multi-class code units in Java and Python, annotated for compliance with five SOLID principles. Feature embeddings were extracted using the three transformer models. These embeddings were input to six different classifiers per principle. We evaluated model performance using stratified 5-fold cross-validation and reported accuracy, precision, recall, and F1 scores.

Results

Principles with well-defined structural characteristics, such as Interface Segregation (ISP) and Dependency Inversion (DIP), achieved high F1 scores (>90%). Semantically complex principles like Single Responsibility (SRP) and Liskov Substitution (LSP) yielded lower F1 scores (∼70–75%). Among the models, StarCoder2 combined with Multi-Layer Perceptron (MLP) consistently outperformed others across both Java and Python datasets. Statistical analyses confirmed that these performance differences are significant. Furthermore, comparisons with open-source large language models (DeepSeek-Coder-V2 and CodeLlama) demonstrated that the approach yields more stable and interpretable results across all principles.

Conclusion

Machine learning models leveraging code-specific embeddings can accurately identify structurally explicit SOLID principles. Code-oriented transformers such as StarCoder2 and LongCoder outperformed the general-purpose model BigBird, especially for principles requiring nuanced semantic understanding. Beyond its experimental contributions, the study provides practical value by enabling automated design-principle assessment in large codebases, reducing manual inspection effort, and offering a foundation for integration into software quality assurance tools and continuous integration pipelines.

SOLID设计原则是面向对象软件开发的基础，促进模块化、可维护性和可伸缩性。在代码中手工验证这些原则通常是耗时且容易出错的，特别是在大型、多语言的项目中。由于对SOLID原则的遵守与软件质量密切相关，因此自动化验证可以显著提高代码的可靠性。目的提出一种基于机器学习的面向对象代码中SOLID原则遵从性自动分类方法。具体来说，我们研究了由三个预训练的转换模型生成的嵌入表示的有效性：LongCoder和StarCoder2都是面向代码的，BigBird是一个通用模型，支持跨Java和Python代码库的特定原则分类。方法编译了一个新的多标签数据集，该数据集由1103个真实世界的Java和Python多类代码单元组成，并根据五个SOLID原则进行了注释。利用三种变压器模型提取特征嵌入。这些嵌入被输入到六个不同的分类器每个原则。我们使用分层5倍交叉验证评估模型性能，并报告准确性、精密度、召回率和F1分数。结果界面隔离（ISP）和依赖反转（DIP）等具有明确结构特征的原则获得了较高的F1分数（>90%）。语义复杂的原则，如单一责任（SRP）和Liskov替代（LSP）产生较低的F1分数（~ 70-75%）。在这些模型中，StarCoder2结合多层感知器（MLP）在Java和Python数据集上的表现始终优于其他模型。统计分析证实，这些性能差异是显著的。此外，与开源大型语言模型（deepseek - code - v2和CodeLlama）的比较表明，该方法在所有原则下产生更稳定和可解释的结果。利用代码特定嵌入的机器学习模型可以准确识别结构明确的SOLID原则。面向代码的转换器，如StarCoder2和LongCoder，优于通用模型BigBird，特别是对于需要细微语义理解的原则。除了它的实验贡献之外，该研究通过在大型代码库中实现自动设计原则评估，减少人工检查工作，并为集成到软件质量保证工具和持续集成管道中提供了基础，从而提供了实用价值。

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引用次数: 0

Requirements-driven analysis of variability in configurable software 对可配置软件中可变性的需求驱动分析

IF 4.3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information and Software Technology

Pub Date : 2026-04-01 Epub Date: 2026-01-07 DOI: 10.1016/j.infsof.2026.108017

Chin Khor, Robyn R. Lutz

Context:

It is difficult, time-consuming, and error-prone to detect misalignments between the variability requirements in configurable software and the source code intended to implement those requirements.

Objective:

The paper reports progress in checking the consistency between variability requirements and their implementation.

Method:

To automate the consistency checking of variability requirements and variability source code, we create a variability model of configurable features and constraints from the requirements specification. We evaluate the consistency of the variability model against a formal representation of the presence conditions controlling variability in the source code. We generate a traceability-rich consistency dashboard for the developer of any misalignments and a minimal set of configurations providing full variability code coverage for variability testing. The approach is implemented in an open-source prototype tool called VarCHEK.

Results:

VarCHEK was evaluated on three diverse, configurable software projects. VarCHEK accurately identified variability requirements not implemented in the source code, found variabilities in the source code not specified in the requirements, and provided more relevant information to the user for troubleshooting and resolving inconsistencies than is currently available.

Conclusion:

This paper describes a new, practical way to automatically identify inconsistencies between the variability requirements specified for configurable software and the source code developed to implement those requirements.

上下文：检测可配置软件中的可变性需求和用于实现这些需求的源代码之间的不一致是困难的，耗时的，并且容易出错的。目的：本文报告了在检查可变性需求及其实施之间的一致性方面的进展。方法：为了自动化可变性需求和可变性源代码的一致性检查，我们从需求规范中创建可配置特性和约束的可变性模型。我们根据源代码中控制可变性的存在条件的形式化表示来评估可变性模型的一致性。我们为开发人员生成一个可跟踪性丰富的一致性仪表板，用于任何不一致，并为可变性测试提供完整的可变性代码覆盖的最小配置集。该方法是在一个名为VarCHEK的开源原型工具中实现的。结果：VarCHEK在三个不同的、可配置的软件项目上进行了评估。VarCHEK准确地识别了未在源代码中实现的可变性需求，发现了未在需求中指定的源代码中的可变性，并为用户提供了比当前可用的更多的有关故障排除和解决不一致的信息。结论：本文描述了一种新的、实用的方法来自动识别为可配置软件指定的可变性需求和为实现这些需求而开发的源代码之间的不一致性。

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引用次数: 0

User-centric requirements prioritization in mHealth applications: Insights from a Discrete Choice Experiment 移动医疗应用中以用户为中心的需求优先级：来自离散选择实验的见解

IF 4.3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information and Software Technology

Pub Date : 2026-04-01 Epub Date: 2026-01-08 DOI: 10.1016/j.infsof.2026.108014

Wei Wang , Hourieh Khalajzadeh , John Grundy , Anuradha Madugalla , Humphrey O. Obie

Context:

Mobile health (mHealth) applications are widely used for chronic disease management, but usability and accessibility challenges persist due to the diverse needs of users. Adaptive User Interfaces (AUIs) offer a promising approach to personalizing interactions and improving user experience. However, their adoption remains limited, partly due to a lack of understanding of how users perceive and evaluate different adaptation strategies. Addressing this gap is crucial for advancing user-centered design and requirements engineering in software systems for health contexts.

Objective:

This study identifies key factors influencing user preferences and trade-offs in mHealth adaptation design.

Method:

A Discrete Choice Experiment (DCE) was conducted with 186 participants living with chronic conditions who regularly use mHealth applications. Each participant completed a series of choice tasks, selecting their preferred adaptation designs from scenarios composed of six attributes with varying levels. A mixed logit model was applied to examine preference heterogeneity. Subgroup analyses were also conducted to explore variations in preferences across age, gender, health condition, and coping mechanism.

Results:

Participants preferred adaptation designs that preserved usability, offered controllability, introduced changes infrequently, and applied small-scale modifications. Conversely, adaptations affecting frequently used functions and those involving caregiver input were generally viewed less favorably. These findings highlight key trade-offs that influence user acceptance of adaptive mHealth interfaces.

Conclusion:

This study employs a data-driven approach to quantify user preferences, identify key trade-offs, and reveal variations across demographic and behavioral subgroups through preference heterogeneity modeling. These insights provide actionable guidance for designing more user-centered adaptive interfaces and contribute to advancing requirements prioritization practices in software engineering—particularly in the context of health technologies.

背景：移动健康（mHealth）应用程序广泛用于慢性疾病管理，但由于用户的不同需求，可用性和可访问性方面的挑战仍然存在。自适应用户界面（AUIs）为个性化交互和改善用户体验提供了一种很有前途的方法。然而，它们的采用仍然有限，部分原因是缺乏对用户如何感知和评价不同适应策略的了解。解决这一差距对于推进卫生环境软件系统中以用户为中心的设计和需求工程至关重要。目的：本研究确定了影响移动医疗适应性设计中用户偏好和权衡的关键因素。方法：对186名经常使用移动健康应用程序的慢性疾病患者进行离散选择实验（DCE）。每个参与者完成一系列的选择任务，从六个不同级别的属性组成的场景中选择他们喜欢的适应设计。采用混合logit模型检验偏好异质性。亚组分析还探讨了不同年龄、性别、健康状况和应对机制对偏好的影响。结果：参与者更喜欢保留可用性、提供可控性、不频繁引入变化和应用小规模修改的适应性设计。相反，影响经常使用的功能和涉及护理人员输入的适应通常不太受欢迎。这些发现突出了影响用户接受适应性移动健康界面的关键权衡。结论：本研究采用数据驱动的方法来量化用户偏好，确定关键的权衡，并通过偏好异质性模型揭示人口统计学和行为亚组之间的差异。这些见解为设计更多以用户为中心的自适应界面提供了可操作的指导，并有助于推进软件工程中的需求优先级实践——特别是在卫生技术的背景下。

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引用次数: 0

A study on functionality validation for windows malware mutating using reinforcement learning 基于强化学习的windows恶意软件变异功能验证研究

IF 4.3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information and Software Technology

Pub Date : 2026-04-01 Epub Date: 2026-01-02 DOI: 10.1016/j.infsof.2025.108008

Do Thi Thu Hien , Le Viet Tai Man , Le Trong Nhan , Phan Ngoc Yen Nhi , Hoang Thanh Lam , Nguyen Tan Cam , Van-Hau Pham

Context:

To keep pace with the rapid advancements in both the quality and complexity of malware, recent research has extensively employed machine learning (ML) and deep learning (DL) models to detect malicious software, particularly in the widely used Windows system. Despite demonstrating promising accuracy in identifying malware, these models remain vulnerable to adversarial attacks, where carefully modified malware samples can bypass detection. Consequently, there is a growing need to generate mutated malware by altering existing samples to comprehensively assess the robustness of ML/DL-based detectors. Unlike in the field of computer vision, functionality validation plays a crucial role in evaluating the effectiveness of these modified malware samples. Even if they achieve high evasion rates, any corruption in file format or execution can make them ineffective.

Objective:

To address this, we consider the essentials of functionality validation in creating malware samples by designing validators that can be used in reinforcement learning-based Windows malware mutation. Our focus is on workable and useful adversarial samples rather than the quantity.

Method:

Two different functionality validation methods are proposed, leveraging the static and dynamic analysis processes of PE files to capture the representation of their behaviors to verify the preservation of designed functionalities. They are then integrated into the RL framework to support the agent in recognizing actions that can cause broken samples.

Results:

Whether employing static or dynamic analysis for validation, the experimental results confirm that the proposed methods successfully maintain the original behavior of malware while enhancing its ability to evade ML-based detectors. Compared to other approaches, although the number of created adversarial malware drops due to stricter validation, a higher ratio of them are confirmed functionality-preserved.

Conclusions:

Functionality validation is an essential task in creating Windows malware mutants to ensure their reliability and usability in further assessment scenarios or real-life attacks.

背景：为了跟上恶意软件质量和复杂性的快速发展，最近的研究广泛使用机器学习（ML）和深度学习（DL）模型来检测恶意软件，特别是在广泛使用的Windows系统中。尽管证明了识别恶意软件的准确性，但这些模型仍然容易受到对抗性攻击，在对抗性攻击中，精心修改的恶意软件样本可以绕过检测。因此，越来越需要通过改变现有样本来生成突变恶意软件，以全面评估基于ML/ dl的检测器的鲁棒性。与计算机视觉领域不同，功能验证在评估这些修改后的恶意软件样本的有效性方面起着至关重要的作用。即使它们实现了很高的逃避率，文件格式或执行中的任何损坏都可能使它们无效。目的：为了解决这个问题，我们通过设计可用于基于强化学习的Windows恶意软件突变的验证器，考虑了在创建恶意软件样本时功能验证的要点。我们的重点是可行和有用的对抗性样本，而不是数量。方法：提出了两种不同的功能验证方法，利用PE文件的静态和动态分析过程来捕获其行为的表示，以验证设计功能的保存。然后将它们集成到RL框架中，以支持代理识别可能导致破坏样本的操作。结果：无论是采用静态分析还是动态分析进行验证，实验结果都证实了所提出的方法成功地保持了恶意软件的原始行为，同时增强了其逃避基于ml的检测器的能力。与其他方法相比，尽管创建的对抗性恶意软件的数量由于更严格的验证而减少，但它们中确认功能保留的比例更高。结论：功能验证是创建Windows恶意软件突变体的基本任务，以确保其在进一步评估场景或实际攻击中的可靠性和可用性。

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引用次数: 0

Introduction to the Special Issue - Agile and Lean: How far did we come and what’s next? 特刊简介-敏捷与精益：我们已经走了多远，下一步是什么？

IF 4.3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information and Software Technology

Pub Date : 2026-03-01 Epub Date: 2025-12-10 DOI: 10.1016/j.infsof.2025.107992

Eduardo Guerra , Darja Smite , Xiaofeng Wang

Over twenty-five years after the Agile Manifesto was introduced, agile and lean practices have matured and become a relevant paradigm for software development. Their widespread adoption has led to documented success cases in the literature, but according to the original manifest signatories, there are also troubling signs of superficial implementation and conceptual misalignments. This introduction to the Special Issue “Agile and Lean: How far did we come and what’s next?” reflects on this evolution and current state of agility in research and practice. The six contributions to this special issue highlight critical themes, including organizational agility, large-scale adoption, team diversity, agile culture, and remote collaboration, exposing existing gaps between agile values and their realization. Based on this, the present introduction also points toward future research directions in agile methods, including topics like hybrid work, cultural maturity, sustaining agility, and integrating AI technologies into agile development. Ultimately, we argued that the strong point of Agile lies not in specific and predictive frameworks and tools but in its human-centered philosophy of collaboration, learning, and continuous improvement.

在敏捷宣言被引入25年后，敏捷和精益实践已经成熟，并成为软件开发的相关范例。它们的广泛采用导致了文献中记录的成功案例，但根据最初的明确签名，也有令人不安的表面执行和概念失调的迹象。“敏捷与精益：我们已经走了多远，下一步是什么？”这篇特刊的引言反映了敏捷在研究和实践中的演变和当前状态。本期特刊的六篇文章强调了一些关键主题，包括组织敏捷性、大规模采用、团队多样性、敏捷文化和远程协作，揭示了敏捷价值观与其实现之间存在的差距。在此基础上，本引言也指出了敏捷方法未来的研究方向，包括混合工作、文化成熟度、持续敏捷性、将人工智能技术融入敏捷开发等主题。最后，我们认为敏捷的优势不在于具体的、可预测的框架和工具，而在于它以人为中心的协作、学习和持续改进的哲学。

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引用次数: 0

On the use of extended reality to support software development activities: A systematic literature review 关于使用扩展现实来支持软件开发活动：系统的文献回顾

IF 4.3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information and Software Technology

Pub Date : 2026-03-01 Epub Date: 2025-12-19 DOI: 10.1016/j.infsof.2025.107999

Tiara Rojas-Stambuk , Juan Pablo Sandoval Alcocer , Leonel Merino , Andres Neyem

Context:

Extended Reality (XR) technologies, including virtual, augmented, and mixed reality, offer novel ways to support software development through immersive and spatial representations of complex software artifacts. Although many XR-based tools have been introduced, their coverage of development activities, types of visualized software data, and evaluation quality remain unclear.

Objectives:

This paper aims to systematically review the use of XR in software development, focusing on the tasks supported, the types of data visualized, the visualization and interaction techniques, the evaluation methods, and the limitations reported.

Methods:

We conducted a systematic review of the literature of 77 primary studies published between 1995 and February 2025. Each study was analyzed and classified according to the supported development tasks, the types of visualized software data, the visualization techniques used, the XR technologies used, the evaluation strategies, and the limitations.

Results:

Our findings show that most XR tools target software comprehension, primarily through structural visualizations. City metaphors and other metaphor-based techniques are the most common. However, XR remains underexplored in activities such as testing, performance analysis, and requirements engineering. Evaluation approaches are heterogeneous, often lacking methodological rigor, sufficient sample sizes, and standardized metrics.

Conclusion:

Although XR holds promise for improving software development, its current use is concentrated in a narrow set of activities and is hampered by limited evaluation quality. The challenges remain in tool integration, interaction design, and practical adoption. We identify key gaps and provide recommendations to guide future research toward broader and more effective use of XR in software engineering.

上下文：扩展现实（XR）技术，包括虚拟现实、增强现实和混合现实，提供了通过复杂软件工件的沉浸式和空间表示来支持软件开发的新方法。尽管已经引入了许多基于xr的工具，但是它们对开发活动、可视化软件数据类型和评估质量的覆盖范围仍然不清楚。目的：本文旨在系统地回顾XR在软件开发中的应用，重点关注支持的任务、可视化数据的类型、可视化和交互技术、评估方法以及报道的局限性。方法：我们对1995年至2025年2月间发表的77篇主要研究文献进行了系统综述。根据支持的开发任务、可视化软件数据的类型、使用的可视化技术、使用的XR技术、评估策略和局限性，对每个研究进行了分析和分类。结果：我们的研究结果表明，大多数XR工具的目标是软件理解，主要是通过结构可视化。城市隐喻和其他基于隐喻的技巧是最常见的。然而，XR在测试、性能分析和需求工程等活动中仍未得到充分的探索。评估方法是异构的，通常缺乏方法的严谨性、足够的样本量和标准化的度量。结论：尽管XR有希望改善软件开发，但它目前的使用集中在一组狭窄的活动中，并且受到有限的评估质量的阻碍。挑战仍然存在于工具集成、交互设计和实际采用方面。我们确定了关键的差距，并提供了建议，以指导未来在软件工程中更广泛、更有效地使用XR的研究。

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引用次数: 0

Sustainability-aware reference architectures: Needs and future research directions 可持续意识参考架构：需求与未来研究方向

IF 4.3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information and Software Technology

Pub Date : 2026-03-01 Epub Date: 2025-12-16 DOI: 10.1016/j.infsof.2025.108000

Elisa Yumi Nakagawa , Rick Kazman

Context:

Reference architectures have significantly contributed to software system development, leading to system standardization, interoperability, and project costs and risk reduction. Although they can considerably promote architectural knowledge reuse, most of them do not remain useful or even survive over the years. As a consequence, the cost, effort, and time designing them are wasted.

Objective:

We introduce the concept of sustainability to the reference architecture field and detail the view of sustainability-aware reference architecture.

Methods:

Based on existing initiatives and evidence from both reference and software architectures, we propose this novel view that contains two sustainability perspectives (of and in) and five sustainability pillars: technical, economic, organizational, social, and environmental.

Results:

Several open issues still exist, so we highlight some breakthrough ideas for future research directions to make the community think.

Conclusions:

Changing the mindset towards this novel view on how to deal with reference architectures is necessary to ensure their long-term value.

上下文：参考体系结构对软件系统开发做出了重大贡献，导致了系统标准化、互操作性以及项目成本和风险的降低。尽管它们可以极大地促进体系结构知识的重用，但它们中的大多数都不会保持有用，甚至无法存活多年。因此，设计它们的成本、精力和时间都被浪费了。目的：将可持续性概念引入参考建筑领域，详细阐述可持续性意识参考建筑的观点。方法：基于现有的倡议和来自参考和软件架构的证据，我们提出了这个新的观点，它包含两个可持续性视角（of和in）和五个可持续性支柱：技术、经济、组织、社会和环境。结果：目前仍存在一些开放性的问题，因此我们对未来的研究方向突出了一些突破性的思路，以引起社区的思考。结论：对于如何处理参考架构的新观点，改变思维模式是确保其长期价值的必要条件。

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引用次数: 0