Can AI Keep Code Agile? The Impact of Language Models on Software Lifecycles

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Author: Denis Avetisyan

A new review examines how large language models are changing the game for software maintenance and evolution, offering both powerful assistance and potential pitfalls.

This systematic literature review assesses the benefits and risks of using large language models for software maintainability and evolvability, identifying key challenges related to technical debt and the need for robust mitigation strategies.

While offering the potential to revolutionize software development, the increasing integration of Large Language Models (LLMs) introduces a complex interplay of benefits and risks to long-term code quality. This study, ‘A Survey on Large Language Model Impact on Software Evolvability and Maintainability: the Good, the Bad, the Ugly, and the Remedy’, systematically reviews the literature to identify how LLMs influence software maintainability and evolvability, revealing improvements in areas like testability alongside emerging threats such as hallucinated outputs and the accumulation of technical debt. Our findings suggest that realizing the full potential of LLMs requires careful mitigation strategies and sustained human oversight to ensure sustainable software systems. How can the software engineering community best navigate this evolving landscape to harness the power of LLMs while safeguarding the long-term health of our codebases?

The Erosion of Software Foundations

Contemporary software development is increasingly defined by a relentless demand for accelerated feature delivery. This pressure, stemming from competitive markets and rapidly evolving user expectations, frequently compels developers to prioritize short-term gains over long-term architectural health. Consequently, practices that bolster maintainability and evolvability – such as comprehensive testing, robust documentation, and thoughtful code design – are often deferred or compromised. While expedient in the immediate term, this trade-off introduces vulnerabilities into the software’s foundation, potentially leading to increased complexity, reduced resilience, and a diminished capacity to adapt to future requirements. The resulting systems can become progressively more difficult and costly to update, ultimately jeopardizing their sustained viability and hindering innovation.

The relentless pressure to deliver software quickly often results in compromises – shortcuts taken during design and implementation to meet immediate deadlines. This practice, known as accumulating ‘TechnicalDebt’, creates a backlog of work that must eventually be addressed to maintain or improve the system. While initially expediting development, this debt compounds over time, making future changes increasingly complex, costly, and error-prone. As the debt grows, the system becomes more brittle, hindering innovation and escalating the risk of critical failures, ultimately impacting the long-term viability of the software.

Software longevity hinges on the attainment of key quality attributes – maintainability, reliability, and adaptability – though consistently achieving these remains a substantial hurdle for developers. Maintainability dictates how easily a system can be modified and extended without introducing errors, while reliability ensures consistent and predictable performance under specified conditions. Adaptability, increasingly vital in a rapidly evolving technological landscape, refers to the system’s capacity to accommodate new requirements or integrate with changing environments. The pursuit of these attributes is often complicated by competing priorities – such as time-to-market pressures and budget constraints – leading to compromises that accumulate over time. Consequently, software projects frequently struggle to balance immediate functionality with the long-term demands of evolution, potentially resulting in systems that become increasingly difficult, costly, and risky to update or extend, ultimately jeopardizing their sustained utility.

Software projects burdened by neglected maintainability and adaptability increasingly face a precarious future. The accumulation of technical debt, coupled with pressures for rapid feature delivery, can transform once-nimble systems into brittle architectures – difficult to modify without introducing new errors. Consequently, even minor updates demand disproportionate effort and expense, escalating maintenance costs and diverting resources from innovation. This escalating cost, combined with a diminished ability to respond to evolving user needs or integrate new technologies, ultimately threatens the long-term viability of the software. Projects failing to prioritize sustainability risk becoming obsolete, requiring complete rewrites, or – in the worst cases – failing catastrophically, impacting dependent systems and the organizations that rely upon them.

Leveraging Language Models for Automated Enhancement

Large Language Models (LLMs) are increasingly utilized to automate core software engineering functions. These models demonstrate proficiency in code generation, producing source code from natural language descriptions or specifications, thereby accelerating development timelines. Furthermore, LLMs facilitate code summarization, creating concise and informative descriptions of existing code blocks to improve understanding and reduce the cognitive burden on developers. Finally, LLMs are capable of automated repair, identifying potential defects within code and proposing or implementing corrective actions, which contributes to improved software quality and reduced technical debt.

Code generation, a key capability of Large Language Models (LLMs), accelerates development cycles by automating the production of code from natural language descriptions or existing code patterns. This automation reduces the manual effort required for coding tasks, leading to faster iteration and deployment. Complementing this is code summarization, which leverages LLMs to produce concise and coherent descriptions of code functionality. These summaries enhance code comprehension, reducing the cognitive load on developers during maintenance, debugging, and collaboration, ultimately improving developer productivity and reducing the potential for errors.

Automated repair capabilities within Large Language Models (LLMs) address software defects through both static and dynamic analysis techniques. Current implementations utilize LLMs to identify potential bugs by analyzing code structure, detecting anomalies, and predicting failure points. Following identification, the LLM proposes and implements code modifications, often leveraging techniques like program synthesis and constraint solving to generate fixes. Evaluation across multiple studies indicates these systems can automatically repair a significant percentage of seeded defects – ranging from 20% to 60% depending on the defect type and complexity – thereby reducing technical debt and improving overall software quality. Furthermore, LLM-driven repair is not limited to simple bug fixes; some systems demonstrate the ability to address more complex issues, including security vulnerabilities and performance bottlenecks.

A synthesis of 87 primary studies indicates Large Language Models (LLMs) possess demonstrable potential to mitigate key bottlenecks within the software development lifecycle. These studies collectively assess LLM performance across various tasks – including code generation, summarization, and repair – and provide empirical evidence suggesting LLMs can improve development speed and reduce associated costs. The reviewed research highlights the scalability of LLM-driven automation, offering a viable path toward enhanced software maintainability by decreasing technical debt and improving code comprehension. Findings consistently show LLMs are not merely theoretical solutions, but present a practical avenue for addressing challenges in modern software engineering practices.

Validating LLM Outputs: A Necessity for Reliability

Large Language Models (LLMs), while offering significant potential for code generation and software development assistance, are prone to generating outputs that are factually incorrect or logically inconsistent – a phenomenon commonly referred to as ‘hallucinations’. These hallucinatory outputs are not random errors; they represent confident assertions that lack grounding in reality or the specified context. The introduction of such inaccuracies into software projects can directly compromise code quality, introduce functional defects, and necessitate increased debugging and testing efforts. Consequently, reliance on LLM-generated code without thorough validation can lead to unreliable software and potentially critical system failures.

Testing frameworks are essential for verifying the functionality and accuracy of code generated by Large Language Models (LLMs). These frameworks facilitate the creation of automated tests that assess LLM outputs against predefined criteria, including functional correctness, adherence to coding standards, and security vulnerabilities. Effective testing involves both unit tests, which validate individual code components, and integration tests, which confirm the interaction between different modules. The implementation of robust testing frameworks helps to identify and mitigate potential defects, reduce the risk of deploying faulty code, and ensure the overall reliability and quality of software applications leveraging LLM-generated code. Furthermore, these frameworks support continuous integration and continuous delivery (CI/CD) pipelines, enabling automated validation with each code change.

Prompt engineering involves carefully designing and refining input prompts to elicit desired outputs from Large Language Models (LLMs). Effective prompt construction focuses on clarity, specificity, and the inclusion of relevant context to guide the LLM towards more accurate and consistent results. Techniques include providing explicit instructions, defining output formats (e.g., JSON, code snippets), utilizing few-shot learning by including example input-output pairs, and employing chain-of-thought prompting to encourage the model to articulate its reasoning process. By strategically manipulating the input prompt, developers can significantly reduce the incidence of hallucinations, improve the relevance of generated content, and increase the overall quality and reliability of LLM-driven applications.

A systematic review of LLM integration into software development identified six overarching risk and weakness themes: data privacy concerns, security vulnerabilities, intellectual property issues, model bias, lack of explainability, and potential for generating incorrect or misleading code. Correspondingly, five mitigation strategies were identified as crucial for addressing these risks: implementing robust data governance policies, employing rigorous security testing, establishing clear intellectual property guidelines, utilizing techniques for bias detection and correction, and incorporating comprehensive validation frameworks to ensure code correctness and reliability. These findings underscore the necessity of proactive risk management and validation procedures when integrating LLMs into software development lifecycles.

Sustaining Software Evolution: A Long-Term Perspective

Ultimately, a software system’s longevity isn’t determined by its initial functionality, but by its SoftwareEvolvability – its inherent capacity to accommodate evolving requirements without succumbing to crippling complexity or fragility. This characteristic moves beyond mere maintainability, encompassing the ease with which new features can be integrated, existing functionalities modified, and the system refactored to leverage new technologies. A truly successful system anticipates change, possessing an architectural flexibility that permits ongoing adaptation, ensuring it remains relevant and valuable long after its initial deployment. Measuring this evolvability-assessing how efficiently and effectively a system can be altered-provides a more meaningful gauge of success than simply tracking lines of code or initial feature counts, recognizing that software is rarely ‘finished’ but perpetually under refinement.

Assessing the enduring impact of Large Language Models (LLMs) on software development necessitates dedicated longitudinal studies. While initial results often highlight productivity gains and streamlined coding processes, the long-term effects on software maintainability and evolvability remain largely unknown. These studies must extend beyond short-term metrics, tracking codebases over several years to observe how LLM-generated code ages, interacts with evolving requirements, and impacts the ease with which future developers can understand and modify the system. Crucially, such research will reveal whether LLM-assisted development ultimately fosters or hinders a system’s ability to adapt, demonstrating whether the initial benefits translate into sustained software health or create technical debt that accumulates over time, impacting long-term project success.

Despite advancements in large language models for software development, profound understanding of the specific application domain remains paramount for achieving optimal results. These models, while capable of generating code, lack inherent contextual awareness; they excel at syntax but often struggle with the nuanced requirements and implicit assumptions unique to each field. Consequently, human experts possessing deep domain knowledge are crucial not only for validating LLM-generated code but also for guiding the models toward solutions that are genuinely fit for purpose and avoid costly errors. This guidance ensures that the software aligns with established best practices, addresses specific industry challenges, and ultimately, remains maintainable and adaptable within its intended context, maximizing the long-term value of the system.

A comprehensive review of recent research reveals five key areas where Large Language Models (LLMs) are positively impacting software development practices. These include accelerated coding, improved code comprehension, streamlined debugging processes, and enhanced code generation capabilities. However, the sustained realization of these benefits requires careful, long-term evaluation. Researchers emphasize the critical need for longitudinal studies-ongoing investigations that track software projects over extended periods-to assess whether these initial gains translate into lasting improvements in software maintainability and evolvability. Without such monitoring, it remains uncertain whether LLM-assisted development will ultimately foster truly adaptable and resilient software systems, or simply offer short-term productivity boosts that are offset by increased technical debt and long-term maintenance challenges.

The systematic literature review highlights a predictable tension: Large Language Models promise gains in software evolvability, yet simultaneously introduce novel forms of technical debt. If the system looks clever, it’s probably fragile. Vinton Cerf observed, “Any sufficiently advanced technology is indistinguishable from magic.” This sentiment applies directly to the current landscape; the apparent ease with which LLMs generate code obscures the underlying complexities and potential for introducing subtle, yet critical, flaws. Architecture, after all, is the art of choosing what to sacrifice, and the trade-off between rapid development and long-term maintainability is becoming increasingly acute with AI-assisted software engineering.

What’s Next?

The surveyed literature reveals a predictable pattern: enthusiasm for a new tool eclipses careful consideration of systemic consequences. Large Language Models offer tempting shortcuts, yet the long-term effects on software ecosystems remain largely uncharted. If these models truly enhance evolvability, it isn’t through sheer code generation prowess, but through a shifting of the burden – from implementation to prompt engineering, from debugging to hallucination detection. This is not progress, but a relocation of complexity.

The field now faces a crucial test. Modularity, often touted as a panacea, becomes a dangerous illusion without a robust understanding of how these models interact with existing codebases. Simply layering AI assistance onto brittle architectures is akin to applying duct tape to a failing engine – it might hold for a time, but the underlying problems persist. True advancement demands a holistic view; a recognition that software isn’t merely a collection of functions, but a complex adaptive system.

Future research should move beyond isolated evaluations of code generation accuracy. The focus must shift to measuring the total cost of ownership – encompassing the effort required for prompt curation, the risk of introducing technical debt through automated changes, and the long-term impact on developer understanding. The question isn’t whether these models can write code, but whether they contribute to systems that can genuinely survive.

Original article: https://arxiv.org/pdf/2601.20879.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-02-01 12:08