Jun 2026· Proceedings of the ACM on Software Engineering· Vol 3, pp. 2652 - 2674· 0 citations· 47 references
TL;DR
This paper conducts the first quantitative study of code suggestion acceptance on large-scale industrial data, and introduces CSAP (Code Suggestion Acceptance Prediction) to predict whether a developer will accept the code suggestion before it is displayed.
Abstract
AI-assisted programming tools are widely adopted, yet their practical utility is often undermined by undesired suggestions that interrupt developer workflows and cause frustration. While existing research has explored developer-AI interactions when programming qualitatively, a significant gap remains in quantitative analysis of developers’ acceptance of AI-generated code suggestions, partly because the necessary fine-grained interaction data is often proprietary. To bridge this gap, this paper conducts an empirical study using 66,239 industrial developer-AI interactions from a large technology company. We analyze features that are significantly different between accepted code suggestions and rejected ones. We find that accepted suggestions are characterized by significantly higher historical acceptance counts and ratios for both developers and projects, longer generation intervals, shorter preceding code context in the project, and older IDE versions. Based on these findings, we introduce CSAP (Code Suggestion Acceptance Prediction) to predict whether a developer will accept the code suggestion before it is displayed. Our evaluation of CSAP shows that it achieves an accuracy of 0.973 and 0.922 on the imbalanced and balanced datasets, respectively. Compared to a large language model baseline and an in-production industrial filter, CSAP improves the accuracy by 12.6% and 69.5% on the imbalanced dataset, and by 87.0% and 140.1% on the balanced dataset. Our results demonstrate that targeted personalization is a powerful approach for filtering out code suggestions with predicted rejection and reducing developer interruption. To the best of our knowledge, it is the first quantitative study of code suggestion acceptance on large-scale industrial data, and this work also sheds light on an important research direction of AI-assisted programming.
Recently, Developers have been relying on AI tools to support them in their daily work by generating code. While the use of large language model-based AI tools has improved productivity, the quality of the generated code wasn't always optimal. In a lot of cases, the code includes design issues known as code smells, which negatively impact readability, maintainability, and future development. This paper investigates these issues in AI-generated Java code, with a focus on common object-oriented problems such as switch statements, temporary fields, and refused bequest. A structured approach is proposed that combines static analysis tools with explainable AI techniques to better understand why these problems appear. Based on the realized insights, prompts are optimized to guide the AI model towards generating cleaner and more structured code. The results showed clear improvement after the prompt optimization, where the number of detected code smells was reduced by 66%, and completely removed (100% reduction) in some cases. Overall, the study showed that improving prompt design, supported by explainable analysis, can significantly enhance the quality of AI-generated code.
Y. Younes, Yousef Elsheikh· IEEE Jordan Conference on Ap...· 0 citations
: We are in a time of change in regards to the emergence of software development as we know it due to the growing number of developers using large language models (LLMs), which eventually will enable major shifts toward the "post-code" era in which software development will become less reliant on coding through using AI-driven development systems that accept natural language and high-level specifications as inputs. This research will analyze the impact of these AI assistants (e.g., GitHub Copilot, Gemini and GPT) through quantitative data collected from Stack Overflow Developer Surveys, GitHub Octoverse Reports, and JetBrains Developer Ecosystem Survey regarding how developers are currently embedding AI into their current practices and what it will look like moving forward. The research found out three things about how developers use Artificial Intelligence. These things are adoption of Artificial Intelligence satisfaction, with Artificial Intelligence the different ways developers are using Artificial Intelligence is changing. The results indicate that there is a distinct directional trend toward AI-native development environments, and that developers are in the midst of rapid change to adopt these tools.
P. Vijayakumar, Jegatheeswari Perumalsamy, Priya Ranjan Parida et al.· Proceedings of the 1st Inter...· 0 citations
The software engineering landscape is undergoing a radical transition from manual syntax craftsmanship to "Vibe Coding"a paradigm defined by prompt-driven, intent-based software generation. This research quantifies the impact of this shift on the software development lifecycle (SDLC), specifically evaluating the efficacy of code review and debugging. Utilizing a diagnostic pipeline with a 50-developer cohort, this study compares traditional Integrated Development Environment (IDE) workflows against AI-assisted "vibe" workflows (utilizing GitHub Copilot and ChatGPT). Our findings demonstrate a profound "Time Displacement Penalty." While initial development velocity approaches zero, severe debugging penalties and elevated bug escape rates in complex systems fundamentally offset these early gains. We identify a "Cognitive Void" wherein developers exhibit high speed and syntax correctness but suffer from a measurable detachment from system architecture and forensic comprehension. This detachment facilitates "Automation Bias" during peer reviews, where the aesthetic perfection of AI-generated syntax masks deep, hallucinated logic failures. The study concludes that while Vibe Coding is optimal for isolated prototyping, it introduces systemic risks in mission-critical architecture. The data mandates a pivot in software engineering pedagogy from syntax authorship to rigorous forensic curation and adversarial auditing.
Prateek Sharma Kharel, Suman Thapalia· International Journal For Mu...· 0 citations
—Code summarization supports program comprehension and defect detection, and developers increasingly use both human-centered and AI-based interventions when performing this task. We present a controlled human-subjects study ( N = 47 ) examining the effects of physical exercise and AI-generated code summaries with varying correctness on developers’ summarization and bug detection performance. Participants summarized GitHub code under different intervention conditions, with outcomes evaluated along multiple dimensions, including accuracy, completeness, conciseness, readability, defect detection, and response time. We analyzed the data using mixed-effects models to account for repeated measures across participants and code artifacts. Surprisingly, under the exercise intervention studied, we did not observe consistent benefits for summarization quality. AI assistance is generally useful for code summarization, but incorrect AI assistance substantially degrades bug detection performance. These findings provide empirical evidence on the nuanced benefits and risks of human-and tool-based interventions in code summarization and bug detection.
Amy Wei, P. Santiesteban, Westley Weimer· IEEE Transactions on Softwar...· 0 citations
It is concluded that AI meaningfully augments developer productivity but does not yet demonstrably improve satisfaction or earnings, and that a hybrid human-AI model, supported by governance and training, remains the most defensible direction for application development.
Perseus Bhavnagri· International Journal for Re...· 0 citations
An empirical study of configuration prompt files in Cursor, a widely used AI-assisted code editor, shows that .cursorrules files emerged rapidly from mid-2024 and shows that there is a continuity of themes and topics between the now-legacy .cursorrules files and the current standard .mdc files.
Shuang Sun, Jafar Akhoundali, Arina Kudriavtseva et al.· Proceedings of the 21st Inte...· 1 citation· ⚡1