arXiv subjects

Anastasiia Serova

Publications and source records attributed to Anastasiia Serova.

4 recordsLinked to original sources

arXiv2026

Evolving with AI: A Longitudinal Analysis of Developer Logs

AI-powered coding assistants are rapidly becoming fixtures in professional IDEs, yet their sustained influence on everyday development remains poorly understood. Prior research has focused on short-term use or self-reported perceptions, leaving open questions about how sustained AI use reshapes actual daily coding practices in the long term. We address this gap with a mixed-method study of AI adoption in IDEs, combining longitudinal two-year fine-grained telemetry from 800 developers with a survey of 62 professionals. We analyze five dimensions of workflow change: productivity, code quality, code editing, code reuse, and context switching. Telemetry reveals that AI users produce substantially more code but also delete significantly more. Meanwhile, survey respondents report productivity gains and perceive minimal changes in other dimensions. Our results offer empirical insights into the silent restructuring of software workflows and provide implications for designing future AI-augmented tooling.

cs.SE↗

arXiv2024

Assessing Consensus of Developers' Views on Code Readability

The rapid rise of Large Language Models (LLMs) has changed software development, with tools like Copilot, JetBrains AI Assistant, and others boosting developers' productivity. However, developers now spend more time reviewing code than writing it, highlighting the importance of Code Readability for code comprehension. Our previous research found that existing Code Readability models were inaccurate in representing developers' notions and revealed a low consensus among developers, highlighting a need for further investigations in this field. Building on this, we surveyed 10 Java developers with similar coding experience to evaluate their consensus on Code Readability assessments and related aspects. We found significant agreement among developers on Code Readability evaluations and identified specific code aspects strongly correlated with Code Readability. Overall, our study sheds light on Code Readability within LLM contexts, offering insights into how these models can align with developers' perceptions of Code Readability, enhancing software development in the AI era.

cs.SE↗

arXiv2024

Reassessing Java Code Readability Models with a Human-Centered Approach

To ensure that Large Language Models (LLMs) effectively support user productivity, they need to be adjusted. Existing Code Readability (CR) models can guide this alignment. However, there are concerns about their relevance in modern software engineering since they often miss the developers' notion of readability and rely on outdated code. This research assesses existing Java CR models for LLM adjustments, measuring the correlation between their and developers' evaluations of AI-generated Java code. Using the Repertory Grid Technique with 15 developers, we identified 12 key code aspects influencing CR that were consequently assessed by 390 programmers when labeling 120 AI-generated snippets. Our findings indicate that when AI generates concise and executable code, it is often considered readable by CR models and developers. However, a limited correlation between these evaluations underscores the importance of future research on learning objectives for adjusting LLMs and on the aspects influencing CR evaluations included in predictive models.

cs.SE↗

arXiv2023

The Effect of Perceptual Load on Performance within IDE in People with ADHD Symptoms

In this paper, we describe the research on how perceptual load can affect programming performance in people with symptoms of Attention Deficit / Hyperactivity Disorder (ADHD). We asked developers to complete the Barkley Deficits in Executive Functioning Scale, which indicates the presence and severity levels of ADHD symptoms. After that, participants solved mentally active programming tasks (coding) and monotonous ones (debugging) in the integrated development environment in high perceptual load modes (visually noisy) and low perceptual load modes (visually clear). The development environment was augmented with the plugin we wrote to track efficiency metrics, i.e. time, speed, and activity. We found that the perceptual load does affect programmers' efficiency. For mentally active tasks, the time of inserting the first character was shorter and the overall speed was higher in the low perceptual load mode. For monotonous tasks, the total time for the solution was less for the low perceptual load mode. Also, we found that the effect of perceptual load on programmers' efficiency differs between those with and without ADHD symptoms. This effect has a specificity: depending on efficiency measures and ADHD symptoms, one or another level of perceptual load might be beneficial. Our findings support the idea of behavioral assessment of users for providing appropriate accommodation for the workforce with special needs.

cs.SE↗