Information and Software Technology最新文献

{"title":"Test automation with selenium: A survey","authors":"Boni García ,&nbsp;Filippo Ricca ,&nbsp;Maurizio Leotta ,&nbsp;Mario Munoz-Organero","doi":"10.1016/j.infsof.2026.108077","DOIUrl":"10.1016/j.infsof.2026.108077","url":null,"abstract":"<div><h3>Context:</h3><div>Selenium is a widely used tool for end-to-end (E2E) web testing. However, it is often criticized for brittleness, slowness, and flakiness. In parallel, newer frameworks and Artificial Intelligence (AI) are reshaping the test automation landscape.</div></div><div><h3>Objectives:</h3><div>This study aims to investigate current practices, challenges, and emerging trends in Selenium-based test automation.</div></div><div><h3>Methods:</h3><div>We designed and executed a large-scale online survey targeting software professionals who use Selenium. The questionnaire covered technical practices, tooling, AI usage, perceived challenges, and competing tools.</div></div><div><h3>Results:</h3><div>A total of 88 complete responses were analyzed using descriptive statistics and thematic coding. The results show that Selenium remains the dominant tool for regression and functional testing, primarily using the Page Object Model (POM) pattern. The most reported challenges are related to assertability, asynchrony, and brittleness. AI tools like ChatGPT are gaining traction for test generation. Playwright is the most prominent alternative.</div></div><div><h3>Conclusion:</h3><div>While Selenium is recognized as a cornerstone in many automation workflows, its limited native test-specific features present a significant drawback. The findings indicate an increasing demand for testing-focused improvements within the Selenium ecosystem, as well as for enhanced integration with AI-driven development tools.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"194 ","pages":"Article 108077"},"PeriodicalIF":4.3,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-gile: Revisiting Agile principles in the era of AI","authors":"Salvatore F. Pileggi,&nbsp;Gnana Bharathy","doi":"10.1016/j.infsof.2026.108073","DOIUrl":"10.1016/j.infsof.2026.108073","url":null,"abstract":"<div><h3>Context:</h3><div>Agile principles have progressively emerged in Software Engineering (SE) to address in fact the intrinsic complexity of modern software developments. The recent rise of AI is generating a disruptive impact in the different disciplines and domains, including also SE.</div></div><div><h3>Objective:</h3><div>We propose an integrated concise analysis of the Agile principles and their possible evolution in the era of AI. We refer this revised version to as <em>AI-gile</em> to put emphasis on the evolving nature of the Agile philosophy, in this specific case as a response to a widely disruptive technological advance.</div></div><div><h3>Methods:</h3><div>We adopted a hybrid analysis method, which combines a traditional critical analysis of major contributions in literature with AI-driven topic modeling. Such an approach enables a seamless semi-qualitative approach to enhance consistency within a naturally interdisciplinary context.</div></div><div><h3>Results:</h3><div>Holistically, our analysis confirms an on-going and potentially radical evolution of Software Engineering as a discipline in response to the incorporation of AI at different levels. Our synthesis indicates that AI’s influence on Agile principles clusters into three recurring patterns: (a) human/AI role re-balancing, (b) collaboration management, and (c) principle erosion and regeneration.</div></div><div><h3>Conclusion:</h3><div>In a context of fast technology evolution, the most significant challenge of AI-gile seems to be related to the rising trade-off between the capability to fully exploit the increasing AI potentialities and the preservation of the human-centric nature of the Agile philosophy.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"194 ","pages":"Article 108073"},"PeriodicalIF":4.3,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SEDMR: A spreadsheet error detection approach based on metamorphic testing","authors":"Bo Yang,&nbsp;Xuelian Hu,&nbsp;Jiatong Ma","doi":"10.1016/j.infsof.2026.108074","DOIUrl":"10.1016/j.infsof.2026.108074","url":null,"abstract":"<div><h3>Context:</h3><div>Spreadsheets are ubiquitous in modern society, used in various domains such as corporate finance, market analysis, and personal budgeting. Despite their widespread use, spreadsheets are prone to errors, which can lead to significant issues, including financial losses. While numerous efforts have been made to detect these errors, existing methods often fail to provide a comprehensive, systematic approach to error detection.</div></div><div><h3>Objective:</h3><div>The primary goal of this research is to develop a more effective and systematic method for detecting errors in spreadsheets. This involves exploring a new research direction using Metamorphic Testing (MT), specifically focusing on defining Metamorphic Relations (MRs) between formula cells and reference cells in spreadsheets.</div></div><div><h3>Methods:</h3><div>We introduce a novel spreadsheet error detection approach called SEDMR (Spreadsheet Error Detection using MRs). SEDMR leverages expanded and tailored MRs that consider both cell data values and formula patterns, offering a comprehensive framework for error detection. The approach includes systematic steps such as extracting spreadsheet cell arrays, selecting MRs, detecting errors, and marking error cells.</div></div><div><h3>Results:</h3><div>SEDMR’s effectiveness is demonstrated through large-scale experiments on extensive datasets, including the EUSES and Enron corpora. The experiments, conducted on 160 spreadsheets, show that SEDMR outperforms state-of-the-art techniques in terms of error detection effectiveness.</div></div><div><h3>Conclusions:</h3><div>The SEDMR approach significantly enhances the efficiency and accuracy of spreadsheet error detection compared to traditional ad-hoc methods. Its scalability and applicability to a wide range of spreadsheet structures make it a versatile solution for real-world scenarios. This research not only provides a robust method for spreadsheet error detection but also opens up promising avenues for future research in this area.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"194 ","pages":"Article 108074"},"PeriodicalIF":4.3,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SRSPSQL: A dual-stage Text-to-SQL framework with semantic rewriting and schema pruning","authors":"Jianjun Lei ,&nbsp;Zhichao Li ,&nbsp;Ying Wang","doi":"10.1016/j.infsof.2026.108064","DOIUrl":"10.1016/j.infsof.2026.108064","url":null,"abstract":"<div><div>Large language models (LLMs) have advanced the Text-to-SQL task significantly. However, existing methods still face challenges in handling natural language ambiguity, limited dataset content, and redundant database schema information. In this paper, we propose SRSPSQL, a dual-stage Text-to-SQL framework that integrates question semantic reconstruction and dynamic schema pruning to eliminate natural language ambiguity and minimize schema search space. Specifically, we design a novel semantic reconstruction mechanism leveraging LLMs’ summarization capabilities and concrete database content to eliminate ambiguities and align questions with database structures. Moreover, we develop a full-schema prompt strategy that adopts masked similarity matching for context-aware example selection and integrates database content information to minimize value errors. Furthermore, we propose a concise schema generation method that combines structured SQL information with dynamic schema pruning to focus on key schema and examples, thereby significantly improving the accuracy and efficiency of SQL generation. Experimental results demonstrate that SRSPSQL consistently outperforms baselines, achieving 86.4% and 62.13% execution accuracies (EX) on Spider and Bird, with robust gains on Spider-Syn and Spider-Realistic. Ablation studies further validate the individual and synergistic contributions of each component.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"194 ","pages":"Article 108064"},"PeriodicalIF":4.3,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring and characterizing cross-service defects in microservice projects","authors":"Chaochao Wu,&nbsp;Ran Mo,&nbsp;Wei Ding,&nbsp;Haopeng Song,&nbsp;Zengyang Li,&nbsp;Yutao Ma","doi":"10.1016/j.infsof.2026.108063","DOIUrl":"10.1016/j.infsof.2026.108063","url":null,"abstract":"<div><h3>Context:</h3><div>In recent years, microservice architectures have attracted much attention as an approach to developing scalable and maintainable software systems. However, there is no systematic research on cross-service defects in microservice projects.</div></div><div><h3>Objective:</h3><div>To fill this gap, we conducted an empirical study of microservice projects from GitHub. This study aims to understand the types of cross-service defects and their involved fixing strategies in microservice projects through a comprehensive commit analysis.</div></div><div><h3>Method:</h3><div>Starting with an initial set of 3551 microservice-related repositories, we rigorously filtered and verified the projects to identify 78 projects that fully implemented microservice architectures. We extracted and analyzed 176 commits from these projects to gain insights into their cross-service defect patterns and fixing strategies.</div></div><div><h3>Results:</h3><div>Through analysis, we identified 10 types of cross-service defects, including service configuration defects, service build and dependency defects, service functionality defects, service communication defects, and service deployment defects. Based on specific commits, we summarized 13 fixing strategies, such as dependency, configuration, and method modifications, to examine the strategies utilized by microservice architectures to fix cross-service defects.</div></div><div><h3>Conclusion:</h3><div>We believe our study could contribute to the understanding of microservice development and offer a foundation for future research in this rapidly evolving field.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"194 ","pages":"Article 108063"},"PeriodicalIF":4.3,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeepFlaky: Deep hybrid representation learning for flaky test prediction","authors":"Jingwen Cai,&nbsp;Yan Lei,&nbsp;Zheyuan Li,&nbsp;Chunyan Liu,&nbsp;Huan Xie,&nbsp;Zhenyu Wu","doi":"10.1016/j.infsof.2026.108070","DOIUrl":"10.1016/j.infsof.2026.108070","url":null,"abstract":"<div><h3>Context:</h3><div>Flaky tests, which exhibit non-deterministic pass or fail behavior without code changes, pose a significant challenge to continuous integration. Existing detection approaches either rely on manually engineered features or deep semantic representations but fail to effectively integrate them. These approaches still suffer from limited representational quality and lack mechanisms to enhance feature space, leading to weakly discriminative representations.</div></div><div><h3>Objective:</h3><div>To overcome the limitations of existing approaches that rely on a single source of features and lack mechanisms to learn robust representations from limited labeled data, we propose DeepFlaky. It not only fuses the interpretability of expert features with the contextual richness of deep semantic representations but also introduces contrastive learning to explicitly structure the feature space, thereby learning more discriminative and generalizable representations for flaky test prediction.</div></div><div><h3>Method:</h3><div>DeepFlaky extracts expert features and semantic representations from test code using CodeBERT. A contrastive learning module optimizes hybrid representations by aggregating positive samples and separating negative ones. The enhanced representation is finally fed into five classifiers for prediction.</div></div><div><h3>Results:</h3><div>Experiments on a widely-used public dataset demonstrate that DeepFlaky outperforms state-of-the-art baseline approaches. It achieves Precision, Recall, and F1-Scores of 93%, 93%, and 93%, with XGBoost performed the best in terms of the evaluation metrics. Ablation studies confirm the individual contributions of expert features, semantic features, and contrastive learning. Overlap analysis shows that DeepFlaky identifies 67 to 282 additional unique flaky tests missed by prior models, including 41 flaky tests not detected by any of the baseline models. Furthermore, DeepFlaky exhibits relatively better generalization in cross-project scenarios compared to other methods.</div></div><div><h3>Conclusion:</h3><div>DeepFlaky demonstrates the complementarity between expert features and semantic features, as well as the effectiveness of contrastive learning in enhancing the quality of code representation. It offers a robust and generalized solution that can effectively capture complex flaky patterns.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"193 ","pages":"Article 108070"},"PeriodicalIF":4.3,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI Act high-risk AI compliance challenge and industry impact: A multiple case study","authors":"Matthias Wagner,&nbsp;Qunying Song,&nbsp;Markus Borg,&nbsp;Emelie Engström,&nbsp;Michal Lysek","doi":"10.1016/j.infsof.2026.108067","DOIUrl":"10.1016/j.infsof.2026.108067","url":null,"abstract":"<div><h3>Context:</h3><div>The AI Act marks a new chapter in AI governance, affecting companies around the world seeking to offer their services within the European Union. This study focuses on the comprehensive AI Act requirements set out for high-risk AI systems.</div></div><div><h3>Objectives:</h3><div>We explored the perceived compliance challenge for the AI Act’s high-risk requirements and associated contributing factors; the AI Act’s impact on industry in terms of positive and negative side effects; and the sentiment of industry practitioners towards the AI Act’s codes of conduct for the voluntary application of the act’s high-risk AI requirements.</div></div><div><h3>Method:</h3><div>A multiple case study encompassing six case companies supplemented by three independent experts with a total of 16 respondents was conducted.</div></div><div><h3>Results:</h3><div>A ranking represents the different perceived levels of challenge for each AI Act high-risk requirement. The ranking is led by the following requirements, starting with the most challenging one: (1) data quality and governance (Art 10), (2) accuracy, robustness, and cybersecurity (Art 15), (3) risk and quality management system (Art 9, 17), and (4) transparency (Art 13). Moreover, four contributing factors emerged that impact the perceived compliance challenge: (1) industry and brand values, (2) existing regulatory environment, (3) AI maturity level and proficiency, and (4) company size. We identified several general key factors for the AI Act’s impact on industry and outlined strong arguments both for and against the AI Act voiced by practitioners. The sentiment towards the AI Act’s codes of conduct turned out very positive.</div></div><div><h3>Conclusion:</h3><div>This study offers a valuable primary research contribution to software engineering, where the state-of-the-art remains short of compliance-oriented studies with a focus on the operationalization of certain AI Act aspects. Future work is advised to develop artifacts facilitating AI Act operationalization and to validate them with industry partners.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"194 ","pages":"Article 108067"},"PeriodicalIF":4.3,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SAME: A Similarity Analysis Method for Evaluating Metamorphic Relations in Testing AI systems","authors":"Zhehao Li ,&nbsp;Jinfu Chen ,&nbsp;Xiaodong Xie ,&nbsp;Tsong Yueh Chen ,&nbsp;Saihua Cai","doi":"10.1016/j.infsof.2026.108069","DOIUrl":"10.1016/j.infsof.2026.108069","url":null,"abstract":"<div><h3>Context:</h3><div>Metamorphic Testing (MT) is a technique that employs Metamorphic Relations (MRs) to act as test oracles to verify software correctness. An effective evaluation of the similarity among MRs is crucial for selecting a diverse and effective set of MRs to maximize fault detection capabilities. Yet, in AI domains, the similarity among MRs remains underexplored. Existing MR similarity methods are complex and do not generalize well to AI tasks.</div></div><div><h3>Objective:</h3><div>We propose SAME (<strong>S</strong>imilarity <strong>A</strong>nalysis <strong>M</strong>ethod for <strong>E</strong>valuating MRs), a lightweight clustering-based approach to quantify MR similarity and reveal hidden structures, offering a fresh perspective for AI system testing.</div></div><div><h3>Method:</h3><div>SAME uses hierarchical clustering to analyze MR-violation vectors produced by MT. We evaluate it on three application domains: (1) object detection (COCO2017 with YOLOv7), (2) multimodal captioning (Flickr30k/COCO2017 with BLIP2, GIT, OFA), and (3) natural-language inference (SNLI/MNLI/ANLI with BERT/RoBERTa).</div></div><div><h3>Results:</h3><div>Compared to the existing method, SAME increases the Adjusted Rand Index (ARI) by up to 0.45 (0.95 vs 0.50) and the Adjusted Mutual Information (AMI) by up to 0.32 (0.91 vs 0.59). Pairwise ARI across six dataset-model pairs, and comparison across different clustering algorithms, show that SAME’s groupings are insensitive to data or algorithm choice but strongly reflect model behavior.</div></div><div><h3>Conclusion:</h3><div>SAME is simpler than prior methods yet more accurate. Its violation-vector design faithfully captures model behavior, exposing redundant MRs (e.g., high-parameter crop <span><math><mo>≈</mo></math></span> stretch) and subtle bias links (e.g., ”elderly” <span><math><mo>↔</mo></math></span> ”wealthy”). These insights provide a new analytical lens for MT results.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"194 ","pages":"Article 108069"},"PeriodicalIF":4.3,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating formal methods and automated tools for DO-178C compliance in UAV software","authors":"Rim Zrelli ,&nbsp;Henrique Amaral Misson ,&nbsp;Sorelle Kamkuimo ,&nbsp;Maroua Ben Attia ,&nbsp;Abdo Shabah ,&nbsp;Felipe Gohring de Magalhaes ,&nbsp;Gabriela Nicolescu","doi":"10.1016/j.infsof.2026.108068","DOIUrl":"10.1016/j.infsof.2026.108068","url":null,"abstract":"<div><h3>Context:</h3><div>The development of software for Unmanned Aerial Vehicles (UAVs) is governed by stringent safety-critical regulations, with DO-178C serving as the primary standard for airborne systems. Ensuring compliance requires extensive verification, validation, and traceability across the software lifecycle, which becomes increasingly complex for autonomous and adaptive UAV functions.</div></div><div><h3>Objective:</h3><div>This paper proposes an integrated methodology for regulatory compliance checking that combines formal methods with automated verification tools to generate certification-ready evidence under DO-178C.</div></div><div><h3>Methods:</h3><div>Formal methods are applied at multiple levels: Alloy is used for requirements consistency checking, the SPIN model checker for architectural interaction properties, and bounded model checking for code-level analysis. These techniques are integrated with automated toolchains that provide continuous bidirectional traceability, structural coverage analysis, and automated test execution across the software lifecycle. The approach is evaluated on a Design Assurance Level (DAL) B UAV Collision Avoidance System.</div></div><div><h3>Results:</h3><div>The case study demonstrates the production of certification-ready evidence bundles, including closed bidirectional traceability from system requirements through high and low-level software requirements to source code and tests, formal proof summaries linked to requirements, and decision coverage reports on mission-critical logic. Results indicate that tightly integrating formal analysis with automated verification improves early defect detection, reduces manual evidence assembly, and strengthens the auditability of DO-178C compliance.</div></div><div><h3>Conclusions:</h3><div>The combined use of formal methods and automation offers a scalable pathway for UAVs and other autonomous systems to achieve compliance with evolving safety regulations. The findings highlight that integrating regulatory compliance checking into development processes can simultaneously enhance rigor and efficiency, providing a model for certifiable autonomy software in civil airspace.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"194 ","pages":"Article 108068"},"PeriodicalIF":4.3,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Empirical insights on interoperability in digital twins: Challenges & LCIM perspectives","authors":"Sarthak Acharya ,&nbsp;Yueqiang Xu ,&nbsp;Nirnaya Tripathi ,&nbsp;Tero Päivärinta ,&nbsp;Arif Ali Khan","doi":"10.1016/j.infsof.2026.108048","DOIUrl":"10.1016/j.infsof.2026.108048","url":null,"abstract":"<div><h3>Context:</h3><div>Digital twins (DTs) have become integral in diverse cyber–physical production systems (CPPS), enabling dynamic interactions between physical entities and their digital counterparts. Yet their integration into such complex ecosystems raises substantial interoperability challenges. While these challenges and associated frameworks for DTs have been extensively theorized in scholarly literature, there are limited empirical investigations that capture industrial perspectives on these aspects.</div></div><div><h3>Objective:</h3><div>This exploratory study aims to empirically investigate real-world interoperability challenges in DT deployments and assess the relevance of a layered interoperability framework as a structured approach to address these issues.</div></div><div><h3>Methods:</h3><div>We addressed this gap by conducting interviews with 12 DT practitioners from 10 companies across five European countries. Interviewees are guided through two reference models: a simplified view of the DT ecosystem and a layered framework based on the Level of Conceptual Interoperability Model (LCIM). The thematic synthesis and systematic mapping of the collected data have used Grounded Theory (GT)-based open coding. Sentiment analysis was used as an illustrative complement to the qualitative findings by capturing expert attitudes towards the LCIM for DTs.</div></div><div><h3>Results:</h3><div>The analysis identified 26 practical interoperability challenges, thematically synthesized into 7 categories. Experts’ perspectives on the LCIM for DTs revealed two key outcomes: 4 drivers of the open and closed-ended nature of interoperability layers, and 4 value propositions highlighting the framework’s relevance for DT deployments. Further, the identified challenge categories are mapped across layers, highlighting the dichotomy of open-source and proprietary approaches, the need for Dynamism and Ecosystem-oriented Interoperability.</div></div><div><h3>Conclusions:</h3><div>This work advances empirical and theoretical understandings of DT interoperability within CPPS. Our findings contribute to addressing practical interoperability challenges, provide empirical values for the layered model in cross-disciplinary approaches to DT integration, and offer guidance for researchers and practitioners. Future work could validate and adapt the layered approach through domain-specific DT applications to assess its effectiveness in digital transformation initiatives.</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"193 ","pages":"Article 108048"},"PeriodicalIF":4.3,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}