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        <title>Frontiers in Computer Science | Software section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/computer-science/sections/software</link>
        <description>RSS Feed for Software section in the Frontiers in Computer Science journal | New and Recent Articles</description>
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        <pubDate>2026-10-13T20:35:55.216+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1919950</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1919950</link>
        <title><![CDATA[From model trust to software topology: a Perspective on structurally governed agentic AI systems]]></title>
        <pubdate>2026-10-08T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Michai Morin</author><author>Fahim Sufi</author>
        <description><![CDATA[Large language models are increasingly embedded in agentic software systems that retrieve information, plan subtasks, call tools, modify code, execute tests, and adapt through memory or feedback. This transition changes the safety problem: when a model acts through software tools, failure can alter external state. This Perspective argues that agentic AI safety should move beyond exclusive reliance on model trust, alignment, or prompt-level defenses and should also be treated as a software-architecture problem. We propose software topology as a design lens for structurally governed autonomy and develop a five-plane reference topology comprising intent, orchestration, execution, oversight, and adaptation. Its core invariant is that components that decide should not directly act, and components that act should not act without supervision. The topology is realized in Polos, an open reference specification that encodes role contracts, message schemas, declared flow graphs, bounded loops, governed self-improvement, and validator-checked invariants. We argue that this approach complements model alignment and empirical red teaming by providing an inspectable substrate for evaluating, constraining, and governing tool-using AI agents.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1911927</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1911927</link>
        <title><![CDATA[Reusing obsolete windows 10 PCs for on-premises large language model inference]]></title>
        <pubdate>2026-10-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ian Curington</author><author>Kevin Lano</author>
        <description><![CDATA[The Windows 10 end-of-life provides a material incentive to repurpose legacy PCs as on-premises large language model (LLM) inference servers. This study assesses the technical feasibility, economic viability and the environmental and social issues of converting an enterprise workstation with an NVIDIA Graphics Processing Unit (GPU) into a dedicated AI node. Using Proxmox virtualisation, Ollama, and Open WebUI to benchmark several open-source LLMs, this study shows that a repurposed workstation, fitted with a single consumer-grade NVIDIA GPU, can deliver stable inference throughput of 29–65 tokens/s for bounded, conversational coding and document-assistant workflows, while preserving full data sovereignty and eliminating recurring subscription fees. Performance analysis reveals that resource contention and GPU memory allocation are the primary determinants of throughput. Correctness, assessed against three established public benchmarks with gold answers, does not track the same ranking as throughput; combining both axes identifies gemma4:e4b as the strongest model evaluated on this hardware for both speed and correctness, while the slowest model tested was among the most accurate. A cost-benefit model shows local operation is substantially cheaper than mid-range and premium cloud alternatives, and only modestly costlier than the cheapest cloud tier on a per-prompt basis—a gap this study argues is outweighed by the digital-sovereignty benefits of local control, particularly where compliance-driven data-handling constraints apply. The study summarizes key background motivations for on-premises AI—privacy, security, regulatory risk—and estimates that this repurposed workstation's usage-phase CO2e footprint is lower than each of the cloud alternatives compared, in addition to avoiding the embodied manufacturing emissions of new hardware. Repurposing legacy workstations with readily available open source tools offers a sustainable, cost-effective pathway for organizations to deploy practical LLM inference capabilities locally, providing a strategic alternative to cloud services for entities that prioritize privacy, regulatory compliance or long-term cost containment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1928487</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1928487</link>
        <title><![CDATA[A same-condition decision-risk audit for CPU-only machine-learning efficiency measurement]]></title>
        <pubdate>2026-10-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Khudran M. Alzhrani</author>
        <description><![CDATA[CPU-only machine-learning efficiency is commonly reported as wall-clock time, throughput, estimated GFLOP/s, or effective bandwidth. This paper introduces a same-condition decision-risk audit that evaluates whether such reported differences are supported by repeated-measurement evidence. The audit constructs two estimates from repeated measurements taken under the same documented condition and quantifies how often their relative difference exceeds a practical relative-difference threshold. We apply the audit to eight CPU-only workload scenarios—dense numerical computation, memory movement, framework-level inference, and single-step training—executed across two local Windows environments and one managed Google Colab CPU allocation. Despite consistent functional-output summaries, timing and derived-metric dispersion depend strongly on workload role, execution context, and phase policy. Under a controlled measurement condition with a fixed phase policy, cross-block same-condition comparisons exceeded a 10% relative-difference threshold in 77.6%–80.6% of cases when reporting a single timing observation, and in 67.1%–76.1% when using the mean of 10 observations, across five representative single-thread scenarios. Approximate sample-size estimates indicate that 81–110 timing observations per group would be needed to support a 10% target relative difference in wall-clock time under the observed variability. The audit demonstrates that CPU-only efficiency differences can only be interpreted meaningfully when the measurement context, phase policy, block structure, same-condition A/A threshold-exceedance, and estimated observation counts are explicitly reported.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1925314</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1925314</link>
        <title><![CDATA[Experimental evaluation of compression algorithms for memory-constrained embedded systems]]></title>
        <pubdate>2026-09-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Tomáš Bača</author><author>Lukáš Formanek</author><author>Peter Ševčík</author><author>Ondrej Karpiš</author>
        <description><![CDATA[IntroductionEmbedded systems are constrained by limited flash memory, RAM (random access memory), and processing power, making efficient local data storage and transmission challenging. This study evaluates the suitability of several lossless compression algorithms for resource-constrained embedded platforms and assesses how data representation influences compression performance.MethodsLZW, Heatshrink, LZ4, FastLZ, and Miniz were evaluated directly on an STM32U575ZI microcontroller (Arm Cortex-M33, 160 MHz). A 104,067-byte sensor dataset comprising repetitive time-series measurements of soil and ambient temperature, humidity, battery voltage, timestamps, and identifiers was used as the test workload. The dataset was stored in three formats: CSV (comma-separated values) (104,067 bytes), binary (33,576 bytes), and bit-packed binary (26,581 bytes). Compression ratio, execution time, and total application RAM consumption, including input and output buffers, were measured.ResultsMiniz achieved the highest compression efficiency, reducing the 33,576-byte binary dataset to 11,352 bytes (33.81% of the original size). The fastest compression was obtained with LZ4 configured with acceleration = 16, which compressed the bit-packed representation in 9.437 ms and produced a 25,692-byte output. Among configurations selected for minimum compressed output, FastLZ achieved the shortest execution time at 31.653 ms. When total RAM consumption was evaluated consistently, including all required buffers, LZ4 operating on the bit-packed representation required the least memory among the minimum-output configurations, consuming 58,496 bytes.DiscussionThe results demonstrate significant trade-offs between compression ratio, execution speed, and memory requirements in embedded environments. Data representation substantially influences compression effectiveness, with binary and bit-packed formats generally improving efficiency over CSV. The findings provide practical guidance for selecting compression algorithms in resource-constrained embedded systems, where minimizing storage, execution time, or memory usage may be prioritized depending on application requirements.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1937190</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1937190</link>
        <title><![CDATA[Replications, Revisions, and Reanalyses: Managing Empirical Evidence in Software Engineering]]></title>
        <pubdate>2026-09-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Julian Frattini</author><author>Jannik Fischbach</author><author>Davide Fucci</author><author>Michael Unterkalmsteiner</author><author>Daniel Mendez</author><author>Robert Feldt</author><author>Richard Torkar</author>
        <description><![CDATA[BackgroundOne aspired outcome of empirical research on quantitative data is a variance theory, i.e., a quantification of the effect of an independent on a dependent variables. In software engineering (SE) research, variance theories quantify—among others—the impact of tools, techniques, and other treatments on software development outcomes like productivity, cost-efficiency, and defect-detection rates. The validity of variance theories stems from the synthesis of multiple pieces of evidence, which increases its validity beyond the findings of a single study.GapHowever, research synthesis in SE is rare and—if done—mostly limited to purely narrative syntheses. At best, researchers perform meta-analyses to synthesize variance theories from several quantitative results. But even meta-analyses only produce reliable results when synthesizing exact replications yet fail to generalize from variations.GoalWe aim to extend the frontier of research synthesis beyond the state-of-the-art to systematically manage empirical evidence and its evolution.MethodWe apply method engineering to construct a framework for research synthesis from proven, individual method fragments. The framework allows researchers to put new evidence in a clear relation to an existing body of evidence and systematically expand knowledge about a studied phenomenon. We demonstrate the application of this framework to two fields of research by explicitly modeling the relationship between existing pieces of evidence.ResultThe framework puts three types of evolution of evidence into relation: (1) replications investigate the same hypothesis in a new context to improve external validity, (2) revisions challenge an existing hypothesis to improve internal validity, and (3) reanalyses replace analysis methods to improve conclusion validity. Through a systematic evolution of evidence and clear assessment criteria for each dimension of validity, the proposed framework can determine the frontier of a field of research.ConclusionThe framework provides a perspective to systematically evolve empirical evidence in SE, supporting more constructive and productive advances in our field.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1883327</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1883327</link>
        <title><![CDATA[Forecasting sports outcomes through machine learning]]></title>
        <pubdate>2026-09-18T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Andres Gregori</author><author>Ivan Reyes-Pedroza</author><author>Ari Yair Barrera-Animas</author><author>Julieta Noguez</author><author>David Escobar-Castillejos</author>
        <description><![CDATA[IntroductionMachine learning has increasingly been applied to forecast sports outcomes, assess player performance, estimate injury risk, and model in-game events, yet the reported evidence remains fragmented across sports and validation practices.MethodsTo address this fragmentation, this systematic review followed PRISMA 2020 reporting principles and analyzed 118 English-language journal articles published between 2020 and 2025 in Scopus and Web of Science, coding each study by prediction domain, sport discipline, algorithm family, dataset type, evaluation metric, and validation approach.ResultsMatch outcome prediction and player performance assessment accounted for most studies, whereas injury prediction and in-game event forecasting were less frequent and drew on smaller per-domain samples. Random Forest, Gradient Boosting, XGBoost, Support Vector Machines, Neural Networks, and Logistic Regression were the most common methods: ensemble models predominated in structured tabular datasets, whereas deep learning was concentrated in video, tracking, temporal, and multimodal contexts. Reported performance was difficult to compare directly because studies used heterogeneous metrics, partitions, datasets, and validation schemes.DiscussionThese patterns indicate that data access, task design, and validation quality matter as much as algorithm choice; future work should therefore prioritize open benchmarks, external validation, and causal modeling, while privacy-aware data governance, uncertainty estimation, and interpretability remain underdeveloped priorities for the field.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1813208</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1813208</link>
        <title><![CDATA[A model-driven framework for automated UI prototyping with the two-hemisphere model]]></title>
        <pubdate>2026-09-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kristaps Babris</author><author>Oksana Nikiforova</author><author>Leszek Maciaszek</author>
        <description><![CDATA[IntroductionAutomated generation of executable user interfaces from domain models remains a challenging problem in model-driven software engineering, particularly when both application behavior and domain structure must be preserved in the resulting interface. Existing model-based approaches often remain at the level of abstract interface specifications, whereas AI-assisted generation provides flexible code synthesis but offers limited deterministic traceability to formal domain models. This study proposes a model-driven framework for automatically generating executable web user interface prototypes from the two-hemisphere model, which integrates process and concept representations of a problem domain.MethodsThe proposed transformation pipeline maps processes to application states and navigation structures, concepts and their attributes to data-oriented interface elements, and process semantics to reusable user interface patterns selected through a deterministic scoring mechanism. A proof-of-concept implementation generates React.js/Material-UI prototypes incorporating routing, state management, CRUD structures, and automatically generated demonstration data. The framework was evaluated using an e-commerce case study through three complementary methods: static source-code analysis, ISO/IEC/IEEE 29119-based functional acceptance testing, and a pilot expert heuristic evaluation based on Nielsen's usability heuristics.ResultsThe generated prototype passed all 25 functional acceptance-test scenarios, required no post-generation source-code modification, and was generated in approximately 12 seconds. Static analysis identified no JSLint or ESLint issues and only two minor JSHint warnings. Three independent usability evaluators produced 22 individual observations, which were consolidated into nine unique usability problems: three cosmetic, five minor, one major, and no critical violations. The major issue concerned the absence of a clearly visible order summary in the generated shopping-cart interface.DiscussionThe results provide preliminary evidence that the two-hemisphere model can serve as a viable source representation for generating executable user interface prototypes while preserving traceability between behavioral and structural domain information and the resulting implementation. The heuristic findings also identify concrete opportunities for improving transformation rules and the user interface pattern library. However, broader generalisability, comparative efficiency, accessibility, and end-user usability remain to be established through multi-case and user-centred evaluation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1886850</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1886850</link>
        <title><![CDATA[Monitoring and comparative analysis of containerized environments]]></title>
        <pubdate>2026-08-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Assel Omarbekova</author><author>Lazzat Kussepova</author><author>Altanbek Zulkhazhav</author><author>Mamyr Altaibek</author><author>Gulmira Bekmanova</author><author>Aizhan Nazyrova</author><author>Zhanar Lamasheva</author>
        <description><![CDATA[This paper systematically examines monitoring tools for containerized applications, microservices, and DevOps environments, and provides an experimental evaluation across two deployment scenarios. The study was conducted across two infrastructures: on-premises (Docker Swarm, Kubernetes) and cloud-based (Google Kubernetes Engine). A test application was deployed, and load and stress tests were performed using Apache JMeter and k6 with virtual user counts ranging from 100 to 1,000. Metrics including CPU, memory, I/O, and network utilization were collected using Prometheus, Grafana, cAdvisor, and Docker stats. Based on exploration and evaluation, 69 unique monitoring-related tools were identified and grouped into four non-mutually exclusive categories: container-based, cloud-based, microservices-oriented, and DevOps-focused (18, 25, 13, and 38 assignments, respectively; 94 assignments in total because some tools belong to multiple categories). A classification is presented according to visualization capabilities, supported metrics, and quality attributes, including performance, security, interoperability, and usability. Experimental results demonstrate that both monitoring stacks captured variations in processor utilization under load at the resolution configured by their default sampling intervals (15–30 s for Prometheus + cAdvisor and 60 s for Google Cloud Monitoring), with the 1,000-virtual-user stress bursts visible as sharp CPU peaks in both stacks. The contribution of this work is a practical guideline for selecting monitoring tools, developed on the basis of a reference experimental environment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1936730</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1936730</link>
        <title><![CDATA[Software quality assurance in the era of Agentic AI: a systematic mapping study]]></title>
        <pubdate>2026-08-28T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Randa Ouaarous</author><author>Imane Hilal</author><author>Abdellatif Mezrioui</author>
        <description><![CDATA[The growing complexity of software systems has increased demand for intelligent solutions in Software Quality Assurance (SQA). Agentic Artificial Intelligence (AI) offers autonomous decision-making, adaptive behavior, and proactive quality improvement, yet its applications across SQA remain fragmented. This systematic mapping study examines Agentic AI integration in SQA, analyzing application distribution, agent characteristics, and supported stakeholder roles to identify coverage, trends, benefits, limitations, and gaps. Following systematic mapping protocols and PRISMA, five digital libraries were searched, retrieving 37 primary studies. Data were classified through frequency analysis and qualitative categorization aligned with Agentic AI behavior, architecture, and autonomy dimensions. Applications concentrate in Product Assurance, mainly test generation and defect management, with fewer studies in Process or Planning activities. Most agents exhibit goal-oriented or diagnostic behaviors, moderate autonomy, and hybrid or multi-agent architectures. Benefits include efficiency, accuracy, and maintainability improvements; limitations involve scalability, generalizability, and transparency. Role coverage focuses on execution-level stakeholders, while managerial and leadership roles receive minimal support. Agentic AI shows potential for strengthening SQA intelligence and adaptability but remains unevenly distributed across activities and roles. Transparency, validation, and leadership support are priority research gaps for developing trustworthy, process-aware Agentic systems across the full SQA lifecycle.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1919566</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1919566</link>
        <title><![CDATA[An automated explainable decision-support system for multi-criteria assessment of functionalized implant coatings]]></title>
        <pubdate>2026-08-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Karlygash Alibekkyzy</author><author>Madina Batalova</author><author>Paweł Komada</author><author>Saule Belginova</author><author>Madina Bazarova</author><author>Talshyn Keribayeva</author><author>Indira Karymsakova</author><author>Yergazy Zheksenov</author>
        <description><![CDATA[IntroductionThe selection of functionalized implant coatings is a complex multi-criteria decision-making problem because biological, engineering, technological, and implementation-readiness indicators are heterogeneous and are frequently evaluated manually. This study proposes an automated and explainable computational decision-support framework for coating assessment and ranking.MethodsThe framework integrates data preprocessing, indicator normalization, weighted scoring, implementation-readiness assessment, sensitivity analysis, explainable classification, and visualization. Biological effectiveness, engineering and operational reliability, and manufacturability and scalability were used as the three evaluation domains. Published experimental data from 2015 to 2025 on titanium oxide nanostructures, hydrogel coatings, hybrid nanocomposites, and bioactive surface modifications were used for validation.ResultsThe framework generated reproducible domain scores, integral performance scores, implementation classes, and explainable rankings. TiO₂-Sr, Grad-Ti, and TiO₂-NT achieved the highest scores because of their balanced biological performance, engineering reliability, sterilization compatibility, and technological scalability. The ranking remained stable under variations in weighting factors.DiscussionThe proposed framework transforms heterogeneous biomedical data into a transparent and reproducible decision-support workflow. It can serve as the computational core of future biomedical material-selection platforms and support preliminary coating screening, technology-readiness evaluation, and data-driven engineering decisions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1877426</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1877426</link>
        <title><![CDATA[AI-driven automation for CI/CD pipelines using attention-enabled reptile meta learning]]></title>
        <pubdate>2026-07-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Graeson Joshua Elijah</author><author>Mythily M</author><author>Fahmid Al Farid</author><author>Neeraj Addura G R</author><author>Salaja Silas</author><author>Elijah Blessing Rajsingh</author><author>Jia Uddin</author><author>Hezerul Bin Abdul Karim</author>
        <description><![CDATA[Healthcare systems increasingly rely on software to support critical operations such as patient monitoring, diagnostic decision-making, and real-time data management, making reliability, speed, and continuous availability essential. The software development and deployment phases, therefore, play a crucial role in ensuring that the healthcare applications remain stable and compliant with regulatory standards. However, traditional Continuous Integration/Continuous Deployment (CI/CD) pipelines are largely reactive, relying on manual testing, delayed feedback, and post-failure recovery mechanisms, which are time-consuming and prone to errors. Such limitations lead to deployment instability, inefficient resource utilization, and increased operational risk in mission-critical healthcare environments. To overcome these challenges, automated and intelligent approaches are required to enable proactive failure detection, faster recovery, and reliable deployment. In this regard, a novel AI-driven self-adaptive framework, Continuous Integration/Continuous Deployment Attention-enabled Reptile Meta-Learning (CICD-ARML), is proposed. The proposed system continuously learns from pipeline telemetry and adapts to dynamic deployment conditions, reducing dependency on manual intervention. Experimental results demonstrate that CICD-ARML achieves a 96.8% build success rate, reduces mean recovery time by 35%, reduces deployment risk by 59%, and achieves 94% prediction accuracy. These findings highlight the effectiveness of the proposed framework in enhancing the reliability, efficiency, and adaptability of healthcare software deployment systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1816972</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1816972</link>
        <title><![CDATA[An application of selective coding to create a theoretical framework on the factors that influence success or failure in adopting the SIG-UFRN software ecosystem]]></title>
        <pubdate>2026-07-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ailson Medeiros Vasconcelos</author><author>Sandro Ronaldo Bezerra Oliveira</author>
        <description><![CDATA[IntroductionWe call Software Ecosystems (SECOs) the development of software around a common technology platform that results in a set of solutions or services for a consumer market. The Integrated Management System (SIG-UFRN) of the Federal University of Rio Grande do Norte (UFRN) is a technological platform that forms part of the UFRN SECO and is used by a considerable number of Federal Educational Institutions (FEIs) throughout Brazil. The scenarios of use of the technology platform involve a complex mix of technical, social, and business aspects that can lead to the adoption or rejection of the SIG-UFRN. If these aspects are not observed, they can harm the business activities of the FEI, waste resources, and result in penalties for public managers who make poor decisions. This research aimed to construct and validate a theoretical framework that represents the adoption of the SIG-UFRN based on technical, business, and social factors.MethodsTo achieve this objective, Selective Coding, as defined in the Grounded Theory (GT) method, was used, as well as Focus Groups for validation. Selective coding is the third stage of the GT. This refines and integrates concepts to construct a theoretical framework for a phenomenon. This coding process enabled the refinement and interrelation of the concepts inherent in the adoption scenarios of the SIG-UFRN. These concepts were identified in the previous open and axial coding stages of GT. Using techniques such as Storyline Memo, Core Category, and Paradigm Model, a theoretical framework could be constructed representing the adoption of SIG-UFRN.ResultsWe identified a theoretical framework that explained the adoption process of the SIG-UFRN, which was then validated with stakeholders.DiscussionThe results of this study could influence the success or failure of the adoption process and guide stakeholders in adopting SIG-UFRN best practices.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1811944</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1811944</link>
        <title><![CDATA[Vitality assurance in microservice architectures: introducing the health box testing methodology]]></title>
        <pubdate>2026-06-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yusuf Köse</author><author>Sin-Chun Ng</author>
        <description><![CDATA[IntroductionMicroservice architectures introduce complex testing challenges, particularly for grey failures — partial degradation scenarios in which infrastructure signals appear normal while user-facing journeys fail silently. Existing monitoring approaches lack the sensitivity to surface such failures early.MethodsWe introduce Health Box Testing (HBT), a runtime vitality assurance methodology that deploys synthetic Health Agents to execute representative user journeys against production-like environments. A proof-of-concept evaluation was conducted across 12 controlled grey-failure injection trials using Playwright-based agents in a dedicated test environment.ResultsHBT detected all 12 injected grey-failure trials (coverage 100%; median MTTD = 70 s, IQR 48.5-86.8 s). The baseline monitoring approach detected 7 of 12 trials (58.3%), with a conditional median MTTD of 397 s (IQR 344.0-404.5 s). A Wilcoxon signed-rank test on the 7 paired trials confirmed HBT was significantly faster (W = 0, p = 0.01562, two-sided), with a median paired reduction of 322 s.DiscussionHealth Box Testing provides a complementary, toolchain-agnostic framework for continuous vitality assurance that surfaces user-impacting failures earlier than conventional infrastructure monitoring, particularly for grey failures invisible to standard telemetry. Future work includes automated root-cause classification and integration with cognitive-load-aware alerting frameworks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1814498</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1814498</link>
        <title><![CDATA[Platform engineering and internal developer portals: a multivocal literature review]]></title>
        <pubdate>2026-05-04T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Mateen Ali Anjum</author>
        <description><![CDATA[Platform engineering has become the dominant approach to managing developer infrastructure at scale, with industry surveys indicating that 94% of organizations have adopted or plan to adopt dedicated platform teams. Despite this rapid practitioner uptake, academic research remains scarce: a systematic search across five major databases identified fewer than a dozen peer-reviewed papers from reputable venues that address platform engineering directly, while gray literature from foundations, vendors, and industry surveys is abundant. This study presents the first multivocal literature review (MLR) of platform engineering and internal developer portals, following published guidelines for including gray literature in software engineering reviews. The review synthesizes 88 sources across both peer-reviewed and gray literature, with sources explicitly tiered by provenance and gray literature assessed using the AACODS framework. Five research questions address the state of the literature, architectural components and patterns, success metrics and KPIs, adoption barriers, and the relationship between platform maturity and developer productivity. The synthesis yields a taxonomy of internal developer portal components grounded in 36 architecture-focused sources, an integrated metrics framework spanning DORA, SPACE, and developer experience dimensions, a comparative analysis of four platform engineering maturity models, and a quantification of the academic–practitioner divide: only 2 of 88 included sources (2.3%) originate from tier-1 venues with platform engineering as their primary topic, while practitioner communities have generated the authoritative definitions, frameworks, and measurement instruments as academic engagement lags by two to three years. A particularly striking gap concerns scorecards, the primary governance mechanism within IDPs, for which no peer-reviewed empirical evidence of effectiveness exists despite widespread commercial adoption. These findings carry implications for both researchers and practitioners. For the research community, the study identifies nine specific opportunities where empirical work is most needed, with a validated PE maturity model and a PE-specific measurement instrument representing the highest-impact contributions. For practitioners, the evidence supports treating platforms as products, combining delivery metrics with developer experience surveys, and designing golden paths as enablers instead of mandates.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1737008</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1737008</link>
        <title><![CDATA[The role of explainability throughout the MLOps lifecycle: review and research agenda]]></title>
        <pubdate>2026-05-01T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Sule Tekkesinoglu</author><author>Matthias Wagner</author><author>Per Runeson</author>
        <description><![CDATA[As Machine Learning Operations (MLOps) adoption accelerates, systematic integration of explainability is imperative for reliability, transparency, and continuous quality assurance. This paper presents a scoping review examining how explainability is integrated across the MLOps lifecycle, encompassing data handling, model development, and deployment. Each phase is further analyzed through its subareas: data handling (data quality, data pre-processing, and data management), model development (training and pre-deployment auditing), and deployment (developer oversight and end-user interfacing). We identified several key touchpoints within each subarea where XAI methods address specific technical and operational challenges. The synthesis covers a wide range of topics, from explainable imputation and data filtering to fairness auditing in high-stakes decision-making. Findings reveal that although explainability is widely applied, it remains fragmented, with insufficiently validated reliability, and limited operationalization for regulatory compliance. Building on this analysis, we propose a research agenda for embedding continuous explainability throughout MLOps pipelines. Key directions include connecting explainability touchpoints across lifecycle phases, validating the reliability of XAI methods, and operationalizing explainability to meet regulatory requirements such as those defined in the EU AI Act. By framing explainability as an infrastructural mechanism for assurance rather than a post-hoc diagnostic feature, this work advances a lifecycle-spanning perspective on trustworthy and governable AI systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2026.1845840</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2026.1845840</link>
        <title><![CDATA[Editorial: Software specification and verification: models and tools]]></title>
        <pubdate>2026-04-22T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Vincenzo Arceri</author><author>Nabendu Chaki</author><author>Agostino Cortesi</author><author>Novarun Deb</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2025.1655377</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2025.1655377</link>
        <title><![CDATA[Whole-value analysis by abstract interpretation]]></title>
        <pubdate>2026-01-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Luca Negrini</author>
        <description><![CDATA[Value analysis is the task of understanding what concrete values a program might compute for each variable or memory region. Historically, research focused mostly on numerical analysis (i.e., value analysis of programs manipulating numeric values), while string analyses have received wider attention in the last two decades. String analyses present a key challenge: reasoning about strings entails reasoning about integer values either used as arguments to string operations (e.g., evaluating a substring) or returned by string operations (e.g., calculating the length of a string). Traditionally, string analyses were formalized with respect to a specific numeric analysis, usually considering constant values or their possible ranges, tailoring definitions, semantic proofs, and implementations to that particular combination, hence hindering the adoption of the analyses in different contexts. This study presents a modular framework to define whole-value analyses (that is, combinations of numeric analyses, string analyses, and possibly other value types computed by a program) by Abstract Interpretation. The framework defines information exchange between the different analyses in the form of abstract constraints, allowing each analysis to perform given only a generic and analysis-independent description of the abstract values computed by other analyses. Adopting such a framework (i) ensures that soundness proofs are still valid when changing the combination of domains used, and (ii) eases implementation and experimentation of different combinations of value analyses, simplifying comparisons between different scientific contributions and augmenting the set of domains an abstract interpreter can use to analyze a program.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2025.1723480</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2025.1723480</link>
        <title><![CDATA[A configurable approach for intra-model inconsistency management in multi-view collaborative modeling]]></title>
        <pubdate>2026-01-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hayder Ali Neamah Alsharuee</author><author>Mohammadreza Sharbaf</author><author>Behrouz Tork Ladani</author>
        <description><![CDATA[IntroductionIn the software development life cycle, collaborative modeling through multiple projective views of a single, shared model is a critical activity that enables effective collaboration among experts and stakeholders. Real-time optimistic collaboration in multi-view modeling allows concurrent modifications but often introduces inconsistencies that must be resolved to achieve an integrated and valid model. Existing inconsistency management methods frequently focus on isolated repairs or offer limited alternatives, lacking support for collaborative dynamics and configurable resolution strategies. This study aims to develop a configurable framework for managing intra-model inconsistencies in real-time multi-view collaborative modeling environments.MethodsWe propose a novel framework for inconsistency management tailored to multi-view collaborative modeling, based on Model-Driven Engineering (MDE) principles. The framework supports real-time modeling scenarios and enables change propagation according to the online collaboration mode. Key components include a consistency oracle and incremental consistency checking, which together manage the integration of model changes and overlaps. We introduce the COMIM approach, which assists collaborators in handling inconsistencies by considering team interactions, individual ownership, and configurable repair strategies.ResultsThe framework was evaluated through a case study involving multi-view collaborative modeling sessions. Empirical results demonstrate the feasibility and effectiveness of the COMIM approach in maintaining consistency during concurrent modeling activities. The system performed efficiently for teams of up to seven concurrent users, successfully managing change propagation, detecting inconsistencies incrementally, and supporting configurable resolution aligned with collaborative priorities.DiscussionThe proposed framework effectively addresses the complexities of repairing inconsistencies across diverse software models in a collaborative setting. By emphasizing collaborative dynamics, our approach advances traditional inconsistency management methods, which often lack personalization and configurability. Future work may explore scalability to larger teams and adaptation to additional modeling paradigms.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2025.1710121</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2025.1710121</link>
        <title><![CDATA[Enhancing RAPTOR with semantic chunking and adaptive graph clustering]]></title>
        <pubdate>2026-01-12T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yan Liu</author><author>Xiaodong Xie</author><author>Xin Wan</author><author>Yi Pan</author><author>Cheng Wang</author>
        <description><![CDATA[IntroductionWhile Retrieval-Augmented Generation (RAG) enhances language models, its application to long documents is often hampered by simplistic retrieval strategies that fail to capture hierarchical context. Although the RAPTOR framework addresses this through a recursive tree-structured approach, its effectiveness is constrained by semantic fragmentation from fixed-token chunking and a static clustering methodology that is suboptimal for organizing the hierarchy.MethodsIn this paper, we propose a comprehensive two-stage enhancement framework to address these limitations. We first employ Semantic Segmentation to generate coherent foundational leaf nodes, and subsequently introduce an Adaptive Graph Clustering (AGC) strategy. This strategy leverages the Leiden algorithm with a novel layer-aware dual-adaptive parameter mechanism to dynamically tailor clustering granularity.ResultsExtensive experiments on the narrative QuALITY benchmark and the scientific Qasper dataset demonstrate the robustness and domain generalization of our framework. Our full model achieves a peak accuracy of 65.5% on QuALITY and demonstrates superior semantic validity on Qasper, significantly outperforming the baseline. Comparative ablation studies further reveal that our graph-topological approach outperforms traditional distance-based, density-based, and distribution-based clustering methods. Additionally, our approach constructs a dramatically more compact hierarchy, reducing the number of required summary nodes by up to 76%.DiscussionThis work underscores the critical importance of a holistic, semantic-first approach to building more effective and efficient retrieval trees for complex RAG tasks.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcomp.2025.1760117</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcomp.2025.1760117</link>
        <title><![CDATA[Editorial: Human-centered approaches in modern software engineering]]></title>
        <pubdate>2026-01-06T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Javed Ali Khan</author>
        <description></description>
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