2025· International Journal of Artificial Intelligence & Digital Transformation· Vol 8, pp. 01-15· 0 citations
TL;DR
This article presents a well-organized methodology for crafting and handling instruction files so as to achieve a higher level of uniformity and efficiency in AI-supported developers' operations.
Abstract
In software development, AI has become a tool with a wide range of capabilities, from generating code to helping with debugging and even writing documentation. Still, a major puzzle for developers is that the outputs coming from AI can be quite inconsistent and also unpredictable at times. And this is primarily because inputs from developers are usually vague or unstructured. This also means that the role of well-crafted instruction files is absolutely essential in determining the behavior of AI that is reliable and also aware of the context. These instruction files that are structured serve as the medium that connects what a developer wants with what an AI is going to do. They make sure that things are crystal clear, that actions can be done again, and that everything is in line with the standards of the project. Otherwise, using AI may result in outputs that differ not only in the quality but also in the style and correctness, which may cause wastage of time and lessened faith in their usefulness. This article presents a well-organized methodology for crafting and handling instruction files so as to achieve a higher level of uniformity and efficiency in AI-supported developers' operations. By making inputs consistent and incorporating constraints specific to the domain in issue, developers are able to majorly upgrade the quality of their output and at the same time minimize their number of iterations. Studies have shown that through investing in designing strong instructions one not only gets the best out of AI but also is able to facilitate cross-team collaboration more effectively. At the end of the day use of instruction files that are structured is a key element to changing the nature of AI from just being an aid to becoming a reliable partner in the field of software engineering of today.
The paper is trying to deeply analyze the pros and cons of implementing GenAI into software development, analysing current applications used in software development life cycle (SDLC), drawing upon case studies and programmer experience, and examining effect on code quality, team working and project timeline.
P. Arun, Jagdale, Deepti Ameta et al.· Economic Sciences· 0 citations
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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
Software engineering is in the middle of a quiet but far-reaching handover. For most of the last decade, artificial
intelligence in the developer's tool chain meant auto complete: a model that finished a line, or occasionally a function, while
a human wrote and reviewed everything around it. That arrangement has started to break down. Coding agents such as
Claude Code, OpenAI's Codex CLI, Google's Jules, Devin, and Open Hands can now read an entire repository, plan a multifile change, run the test suite, and iterate on failures with little moment-to-moment supervision. The developer's job is
shifting from typing code to directing agents that type code a change often summarized as a move from code generation to
code orchestration. This paper reviews recent empirical literature to ask what that shift is actually producing.
Unknown authors· International Journal of Inn...· 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
It is observed that generated code often omits basic input validation or memory-safety checks, which can lead to overflows, resource exhaustion, or other reliability/security issues, and even the largest models frequently make simple mistakes.
Rodrigo Pato Nogueira, Marco Vieira, João R. Campos· 0 citations