Aug 2026· International Journal for Research in Applied Science and Engineering Technology· Vol 14, pp. 921-936· 0 citations
TL;DR
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.
Abstract
Application development has traditionally depended on manual coding, in which developers write every line of code
themselves. This approach offers precision and control, but it is time-consuming, labour-intensive and prone to human error.
The emergence of Artificial Intelligence (AI) has introduced tools that generate, test, debug and optimise code, raising the
question of how AI-assisted development actually compares with manual practice. This paper presents a comparative study of the
two approaches using secondary data from the Stack Overflow Annual Developer Survey 2024, comprising 65,437 responses
from developers across 185 countries. Seven hypotheses were formulated covering productivity, job satisfaction, accuracy,
compensation, challenges, sentiment and instrument reliability, and were tested using non-parametric methods (Mann-Whitney
U, chi-square) at a 5% significance level. The job-satisfaction scale demonstrated excellent internal consistency (Cronbach's
alpha = 0.931, 9 items, n = 29,095). The analysis found that 57.6% of respondents currently use AI tools, that 81.0% of adopters
identify increased productivity as a benefit, and that 72.0% hold a favourable or very favourable view of AI. However, two widely
assumed advantages did not survive testing. The difference in job satisfaction between AI users and manual coders was
statistically significant but negligible in magnitude (means 6.97 vs 6.89; Cohen's d = 0.039). The apparent compensation
advantage reversed direction once national context was controlled: pooled data showed manual coders earning more, yet within
the United States alone the difference disappeared entirely (p = 0.203), indicating that the pooled gap is a confound arising from
higher AI adoption in lower-income economies rather than an effect of AI itself. Trust remains the principal barrier, with 65.1%
of respondents distrusting AI output and 61.9% reporting that AI tools lack context of their codebase. The study concludes 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
Vibe coding - AI-assisted software development in which the developer describes intent in natural language and validates results by running rather than reading the generated code - was named by Andrej Karpathy in February 2025 and produced its first body of empirical evidence within seventeen months. This state-of-the-art review assembles that evidence across a cross-disciplinary corpus spanning software engineering, human-computer interaction, labour economics, security research, governance, and education. We survey the model landscape, the tool ecosystem, and the performance record by task type, finding the early benchmarks saturated but task-level capability uneven: reliable code generation alongside weak fault detection and hard-to-audit documentation. The productivity record is at first contradictory: peer-reviewed field experiments report +26% more tasks per week, independent randomised trials measure a 19% slowdown, and team-level telemetry shows code-review time up +441%. We argue these readings are consistent once measurement method, scope, and time horizon are held constant, and identify six patterns behind the dispersion, among them effect-shrinkage under broader measurement, self-report diverging from independent measurement, output volume conflated with productivity, and bold claims walked back once tested over longer horizons. We further document security failures in deployed applications, code-quality degradation visible in large-scale code and developer telemetry, unsettled copyright exposure, and evidence of skill atrophy. The review closes with the open research questions and one falsifiable conjecture: that the gains are real on new code and shrink or reverse on mature codebases, which would account for most of the disagreement in the record.
D. Michels, Mutaz Abu Ghazaleh, Francois Lazzari et al.· 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
ABSTRACT Generative AI (GenAI) has advanced rapidly and made significant impacts. However, its effect on developers remains a topic of industry debate. Companies want to know whether GenAI can enhance developers’ coding performance, as an unclear understanding may put companies at a disadvantage. While the literature has begun addressing this issue, a formal understanding of GenAI’s impact remains incomplete. Moreover, existing findings are often short-term, fragmented, or lack explanatory mechanisms. To fill these gaps, we designed a multimethod research program comprising a longitudinal field study and a randomized controlled experiment. In Study 1, we collaborated with a global information technology organization and applied a difference-in-differences approach to over 27 weeks of proprietary data. In Study 2, we designed a randomized experiment involving 253 software developers. From these studies, we find that GenAI usage affects both developers’ coding quantity and quality. These effects, however, depend critically on how the tool is used. While reduced cognitive effort can be associated with diminished quality, interestingly, GenAI usage enables developers to produce higher-quality code with less cognitive effort. In this current study, we explain the paradoxical findings through cognitive load theory, showing that GenAI reduces extraneous load while preserving germane processing during ideation and debugging. Using a multimethod research design that integrates longitudinal field data with a randomized controlled experiment, we link observed performance effects to underlying cognitive mechanisms and usage strategies. We also offer guidance on effective usage styles and propose boundary conditions for realizing GenAI’s benefits in practice.
Guohou Shan, Michael Rivera, Subodha Kumar et al.· Journal of Management Inform...· 0 citations
A codepath-aware governance framework for AI-assisted engineering in regulated codebases, with emphasis on financial services, payments, healthcare, and other domains where software changes may affect legal, operational, privacy, and audit obligations is developed.
Ashutosh Pal· International journal of com...· 0 citations
Generative artificial intelligence (GenAI) is a large language model (LLM) that has the ability to generate media based on user-provided prompts. Given the demonstrated capabilities of models such as ChatGPT in information synthesis and programming, there is growing interest in their potential role within the research process. However, little work has evaluated recent GenAI models for research tasks in the domain of statistical research. This case study examines GenAI as a tool for developing a literature review and translating methodology from academic papers into code, for the topic of dynamic treatment regime (DTR) estimation via the dynamic weighted ordinary least squares (dWOLS) approach. Specifically, we utilize ChatGPT-5 and ScholarAI (Sept-Nov 2025 release) in the processes of identifying relevant sources for the literature review, creating summaries of papers, identifying gaps in research, and R code generation to implement methodology. Our findings show that current GenAI models lack the depth and contextual understanding required to accomplish these tasks without careful prompting and supervision of a knowledgeable researcher. Nonetheless, GenAI has potential to increase efficiency of tasks which take advantage of its search and summarization abilities, as well as basic code debugging and algorithm formation. We demonstrate that under a knowledgeable guide, GenAI can function as a research tool, but not as a substitute for methodological expertise.
Natalie Morosin, A. A. Nadi, M. Wallace· 0 citations
This investigation paves the way for a comprehensive understanding of how AI is perceived by those who directly manage the introduction of these tools into traditional software development workflows, revealing a road map for future endeavors for the software development community.
Xin Zhao, Brian Vu, Sitesh Pattanaik· AIware· 0 citations