Establishing technical debt management — A five-step workshop approach and an action research study

Leveraging multi-task learning to fine-tune RoBERTa for self-admitted technical debt identification and classification

What Happens When Technical Debt Vanishes?

In the evolving landscape of software engineering, managing technical debt has emerged as a critical challenge that compromises software quality and maintainability. This paper presents a structured review of recent advancements in the identification and prioritization of Self-Admitted Technical Debt (SATD) through the application of Natural Language Processing (NLP) and machine learning techniques. By synthesizing the findings from key studies, this paper highlights innovative methods that leverage word embeddings and other NLP models to enhance the automatic detection and resolution of SATD in software projects. We delve into various approaches for extracting and selecting features that accurately categorize technical debt and discuss the development of models that prioritize SATD resolution based on potential impact. Furthermore, the review also compares traditional manual strategies with automated tools, demonstrating significant improvements in efficiency and accuracy brought by AI-driven solutions. This paper aims to provide a comprehensive overview of the state-of-the-art techniques, their practical applications, and the benefits they offer to software development, fostering a deeper understanding of SATD management strategies that can lead to more sustainable software systems. Received: 21 April 2025 | Revised: 10 September 2025 | Accepted: 7 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Satya Mohan Chowdary Gorripati: Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft. Ali Altalbe: Writing – review & editing. Prasanna Kumar Rangarajan: Conceptualization, Validation, Visualization, Supervision, Project administration.

Assumptions about environmental and operational conditions play a key role in the design of sensor-driven and cyber–physical systems. When these assumptions later change or prove incorrect, they can cause rework, inconsistency, and other forms of requirements technical debt (RTD). Although prior studies have highlighted this problem conceptually, there has been limited quantitative evidence showing how assumptions volatility contributes to RTD during early system modeling. Objective: This work introduces the concept of assumptions volatility, the degree to which environmental assumptions evolve or become invalid, and provides the first empirical assessment of how these measures relate to RTD indicators in model-based development. Methods: We analyzed 89 environmental assumptions curated from a prior controlled modeling study. For assumptions volatility, we identified three metrics, i.e., assumption change (ACR), invalidation ratio (IR), and dependence density (DD). These measures were compared against three RTD indicators, i.e., rework ratio, inconsistency density, and correction count. Correlation and regression analyses with robustness checks were used to evaluate the strength and consistency of the observed relationships. Results: Our results showed that assumptions with higher volatility were consistently linked to a greater level of RTD, with dependency density showing the most stable associations among the three volatility measures. Conclusions: The findings provide initial quantitative evidence that environmental assumption volatility is associated with RTD during conceptual design and motivate future multi-domain validation in broader Model-based Systems Engineering settings.

The emergence of open-source ML libraries such as TensorFlow and Google Auto ML has enabled developers to harness state-of-the-art ML algorithms with minimal overhead. However, during this accelerated ML development process, said developers may often make sub-optimal design and implementation decisions, leading to the introduction of technical debt that, if not addressed promptly, can significantly impact on the quality of ML-based software. Developers frequently acknowledge these sub-optimal design and development choices through code comments written during development. These comments, which often highlight areas requiring additional work or refinement in the future are known as self-admitted technical debt (SATD) . While prior research has demonstrated that SATD can serve as a reliable indicator of technical debt and has extensively studied SATD in traditional (non-ML) software, little attention has been given to this issue in the context of ML. This paper aims to investigate the occurrence of SATD in ML code by analyzing 318 open-source ML projects across five domains, along with 318 non-ML projects. We detected SATD in source code comments in various snapshots of the studied projects, conducted a manual analysis of a sample of the identified SATD to comprehend the nature of technical debt in the ML code, and performed a survival analysis of the SATD to understand the evolution dynamics of such debts. Our analyses yielded the following observations: (i) Machine learning projects have a median percentage of SATD that is twice that of non-machine learning projects. (ii) ML pipeline stages for data preprocessing and model generation logic are more susceptible to debt than model validation and deployment stages. (iii) SATDs appear in ML projects earlier in the development process compared to non-ML projects. (iv) Long-lasting SATDs are typically introduced during extensive code changes that span multiple files, which exhibit low complexity. Our research contributes to the understanding of technical debt in an ML context and underscores the need for targeted debt management strategies. This contribution is particularly relevant for developers and stakeholders in ML projects by aiding them in identifying and addressing technical debt proactively and paving the way for future research in developing automated tools and methodologies for managing SATD in an ML environment.

The article is intended to discuss the problem of architectural degradation of distributed web applications under conditions of intensive development, CI/CD processes, and increasing technical debt volume. A formalized approach to identification and monitoring of degradation processes based on the analysis of the dynamics of structural metrics of the architecture is proposed. A model that describes the change in the architectural state over time using metrics of connectivity, cyclomatic complexity, density of dependencies, and other quantifiable parameters is developed. The concept of architectural resilience is introduced, which allows to assess the ability of a system to maintain structural integrity in the condition of external and internal changes. A system of rules based on time series metrics is used to assess degradation, and an indicator of the «architectural shift point» is proposed, as the moment emphasizing restoring of the original structure, that requires significant efforts. The model is validated based on simulated and real data obtained from the CI/CD pipeline. The model is validated using simulated and real data obtained from CI/CD pipelines of three web systems of varying complexity. It is shown that timely detection of architectural degradation allows to increase the efficiency of architectural management, reduce the risks of failures and optimize long-term costs of system maintenance. The results obtained can be used to design architectural dashboards, as well as in order to implement intelligent decision support systems in a DevOps environment.

Open-source software projects face increasing challenges in maintaining design quality as they evolve, often resulting in technical debt accumulation and reduced maintainability. This study explores the relationship between software modularity, measured using ISO/IEC 25010 quality attributes, Chidamber and Kemerer (CK) object-oriented metrics, and architectural smells (AS) in Java-based open-source software. Six Java-based open-source projects were strategically selected based on varying complexity levels (ranging from 6-994 classes) and different application domains to ensure comprehensive analysis coverage using DesigniteJava to extract AS, CK metrics, and modularity indicators. Correlation analyses showed that architectural smells such as Cyclic Dependency, Ambiguous Interface, and God Component are strongly correlated with CK metrics like Weighted Methods per Class, Depth of Inheritance Tree, and Number of Children. These CK metrics also exhibited strong positive correlations with Cyclomatic Complexity, indicating that structurally complex components also tend to have more complex control logic. Dense Structure was found to negatively correlate with Coupling of Components Conformance, suggesting its effect on modularity compliance. On the other hand, smells like Feature Concentration and Scattered Functionality showed weak or inconsistent correlations with these metrics. The findings highlight the importance of addressing specific architectural smells to improve modularity and software quality.

Transitioning to digital management is often framed as a technology refresh, but in practice it is a sociotechnical transformation that alters decision rights, operational controls, accountability models, and the organization’s risk posture. Even when modern platforms are procured and deployed successfully, many organizations fail to realize expected gains in speed, transparency, and performance because constraints originate in governance, legacy architecture, data quality, capability gaps, and external dependencies rather than in software features. This article analyzes the most persistent challenges encountered during transitions to digital management and explains how these challenges reinforce one another through feedback effects such as fragmented ownership, “tool-first” implementation, and transformation fatigue. The discussion focuses on governance and strategic alignment, legacy systems and technical debt, data governance maturity, workforce skills and cultural adoption, cybersecurity and privacy requirements, third-party dependency and resilience expectations, and measurement difficulties that obscure value realization. The article concludes that successful digital management is less about digitizing existing routines and more about redesigning management as an operating model grounded in trusted data, explicit decision rights, and resilient digital operations.

ABSTRACT Introduction Organizations moving from monolithic to microservice architectures face new challenges due to distributed complexity. Architectural Anti‐Patterns (Smells) can arise and contribute to Technical Debt, while current detection approaches remain manual or semi‐automated and prone to error. Methods This study aims to address this gap by proposing an automated detection tool leveraging graph neural networks (GNNs). Microservice systems are modeled as graphs, with services as nodes and their relationships as edges. Graph neural networks (GNNs) are applied to detect four anti‐patterns: (1) cyclic dependencies, (2) enterprise service bus (ESB) usage, (3) microservice greediness, and (4) inappropriate service intimacy. Large language models (LLMs) are used to generate and expand architectural graph datasets to address data scarcity. Results The GNN approach achieves improved detection performance, with up to a 1.2% F1‐score increase over existing tools such as Msanose and Arcan. Conclusion Combining GNNs with LLM‐augmented data enhances automated detection of microservice anti‐patterns and supports more effective architectural assessment.

This article examines the multidimensional journey organizations undertake when transitioning from legacy systems to intelligent digital ecosystems. It establishes a theoretical framework for understanding the historical progression of enterprise architectures while exploring cloud-native paradigms and intelligent system characteristics that define modern technology environments. The article identifies critical strategic challenges in legacy transformation, including technical debt accumulation, data fragmentation, operational rigidity, cultural resistance factors, and security considerations in hybrid environments. An implementation framework is presented, detailing cloud-native re-engineering methodologies, AI-driven automation evolution, data governance models for cross-functional integration, human-centric design principles, and DevOps integration for continuous evolution. The article concludes by evaluating organizational impacts through empirical measurements of operational efficiency, economic implications of intelligent system adoption, cultural transformation metrics, ethical frameworks for responsible AI implementation, and future research directions, including cognitive integration, sovereign clouds, and explainable AI.

Third-party risk management (TPRM) reaches an inflection point, with artificial intelligence (AI) capabilities meeting pressing demands for real-time vendor risk oversight of increasingly complex digital ecosystems. Conventional assessment methodologies resting on manual questionnaires, annual review cycles, and document-centric evaluations are poorly matched to the pace and interconnectedness driving modern technology. This article analyzes how intelligent automation is remaking basic processes in vendor governance, from optimization of questionnaires through semantic modeling to predictive monitoring allowed through continuous data synthesis. Unstructured vendor control documentation is now parsed by natural language models to extract control metadata and produce risk assessments that must be validated, rather than created, by humans. Algorithmic integrity is tackled with multi-model verification architectures that employ parallel processing pipelines where ensemble methods quantify confidence levels and flag gaps in the vendor control environment for risk subject matter expert review. Brain-inspired computing principles underpin system design, with hierarchical feature extraction possible, along with adaptive learning from assessment outcomes. Technical debt becomes a critical governance factor, particularly in the context of data dependencies and configuration management across model lifecycles. Explainable artificial intelligence provides transparency that is vital to regulatory recognition, allowing risk officers to trace decision pathways and understand feature attributions underlying automated recommendations. Convergence of distributed ledger technology with intelligent risk systems unlocks opportunities for tamper-proof audit trails and privacy-preserving attestations in support of cross-organizational governance frameworks framed by emerging digital resilience mandates.

The technical debt gamble: A case study on technical debt in a large-scale industrial microservice architecture

Evaluating time-dependent methods and seasonal effects in code technical debt prediction

This paper defines a microeconomic framework for understanding systemic failure in cyber security as market failure. In a marketplace with limited supply chain transparency on software quality in general and software maintenance in particular, rational actors — both software vendors and software buyers — will maximise economic returns by minimising software maintenance and security. As technical debt accrues, so does vulnerability and operational risk, as systems become more difficult to update. In this regard, the depreciation of resilience in software infrastructure is similar to the breakdown of physical infrastructure that is chronically undermaintained, but with the added element of adversarial profit. These problems cannot be solved at the computer science level that created them. They can only be solved as a business problem, as transparency requirements (eg software bill of materials [SBOMs]) and automation slash the cost of diligence, enable preferential selection of higher-quality software and continuous enforcement of terms and conditions for active maintenance.

Managing technical debt in a multidisciplinary data intensive software team: An observational case study

A practitioner survey on Requirements Technical Debt Quantification

DebtGuard: A Predictive Model For Managing Technical Debt In Agile Development

Machine learning for enterprise deployments in the enterprise context does come with significant challenges in terms of deploying data science to production and requires systematic frameworks in order to be production-ready. The progression from experimental development to operational deployment exposes serious shortcomings in the traditional software engineering practices, as the majority of data science projects fail to successfully move to production because of poor deployment strategies and configuration management problems. AIOps frameworks provide the next generation of solutions that help organizations automate system management, identify failures, and perform remediation steps using artificial intelligence technology, and can result in significantly lower operational overhead. Contemporary software engineering practices must adapt to meet the unique requirements of ML systems, such as specialized version control, continuous integration pipelines, and special methods of technical debt management for data quality, model staleness, and infrastructure complexity. Standardization through self-service platforms offers needed mechanisms to scale AI actions across organizational boundaries and keep operations invariant and the configuration entropy low. The evolution of CI/CD pipelines specifically tailored for machine learning workflows includes flow-based programming paradigms, specialized testing frameworks, and model versioning strategies that help guarantee deployable pipeline reliability and dexterous monitoring capabilities for production-ready systems.

This research uses advanced word embedding techniques to improve the automatic classification of Self-Admitted Technical Debt (SATD). We evaluate how successfully n-gram Inverse Document Frequency (IDF) creates machine learning classifier-friendly feature sets. A publicly available dataset including Java source code comments from 10 open-source projects was used to assess SATD classification methods. This category included Random Forest, SVM, Logistic Regression, and XGBoost. We used instance hardness undersampling to handle the imbalance in the SATD dataset. We tested the classifier using accuracy, recall, F1-score, and Macro-Averaged Mean Cost-Error (MMCE). The Random Forest classifier with n-gram IDF features achieved an average accuracy of 87%. It performed similarly to the traditional TF-IDF and Bag-of-Words methods on average, and on certain projects and MMCE values, it performed better. In rare circumstances, n-gram IDF may reveal contextual phrase patterns and improve SATD recognition, especially when combined with ensemble learning. To enhance generalisability, future research will expand the dataset, investigate hybrid and deep learning models, and increase applicability across various programming languages and project areas. Received: 21 April 2025 | Revised: 12 September 2025 | Accepted: 7 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available on request from the corresponding author upon reasonable request. Author Contribution Statement Satya Mohan Chowdary Gorripati: Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft. Ali Altalbe: Writing – review & editing. Prasanna Kumar Rangarajan: Conceptualization, Validation, Visualization, Supervision, Project administration.