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Artificial Intelligence as a Co-Creator in Software Development.

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Title: Artificial Intelligence as a Co-Creator in Software Development.
Authors: ARSENE, Sabin-Marian
Source: Informatica Economica; Jun2026, Vol. 39 Issue 2, p60-70, 11p
Subject Terms: Artificial intelligence, Computer software development, Defect tracking (Computer software development), Language models, Computer programmers, Prediction models, Software engineering
Abstract: This paper explores the paradigm shift in software engineering by positioning Artificial Intelligence (AI) as an active co-creator within the Software Development Life Cycle (SDLC). While traditional AI tools function as passive assistants, the proposed framework integrates large language models and predictive analytics to foster a collaborative environment between human developers and machine intelligence. Through a Retrieval-Augmented Generation (RAG) approach, the study evaluates improvements in requirements analysis, code generation, and proactive defect detection. Experimental simulations indicate a 57% reduction in lead time and a 37% decrease in defect density, highlighting significant productivity gains. However, the research also identifies the "Reviewer's Paradox," where the cognitive load shifts toward verification and architectural oversight. The findings suggest that AI-human co-creation not only optimizes resource allocation but also necessitates a fundamental redefinition of developer roles in the era of AI-native software engineering. [ABSTRACT FROM AUTHOR]
Copyright of Informatica Economica is the property of Informatica Economica and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
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  Data: Artificial Intelligence as a Co-Creator in Software Development.
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  Data: <searchLink fieldCode="AR" term="%22ARSENE%2C+Sabin-Marian%22">ARSENE, Sabin-Marian</searchLink>
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  Data: Informatica Economica; Jun2026, Vol. 39 Issue 2, p60-70, 11p
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  Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+development%22">Computer software development</searchLink><br /><searchLink fieldCode="DE" term="%22Defect+tracking+%28Computer+software+development%29%22">Defect tracking (Computer software development)</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+programmers%22">Computer programmers</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Software+engineering%22">Software engineering</searchLink>
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  Data: This paper explores the paradigm shift in software engineering by positioning Artificial Intelligence (AI) as an active co-creator within the Software Development Life Cycle (SDLC). While traditional AI tools function as passive assistants, the proposed framework integrates large language models and predictive analytics to foster a collaborative environment between human developers and machine intelligence. Through a Retrieval-Augmented Generation (RAG) approach, the study evaluates improvements in requirements analysis, code generation, and proactive defect detection. Experimental simulations indicate a 57% reduction in lead time and a 37% decrease in defect density, highlighting significant productivity gains. However, the research also identifies the "Reviewer's Paradox," where the cognitive load shifts toward verification and architectural oversight. The findings suggest that AI-human co-creation not only optimizes resource allocation but also necessitates a fundamental redefinition of developer roles in the era of AI-native software engineering. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Informatica Economica is the property of Informatica Economica and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.24818/issn14531305/30.2.2026.05
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        Text: English
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      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Computer software development
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      – SubjectFull: Defect tracking (Computer software development)
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      – SubjectFull: Language models
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      – SubjectFull: Computer programmers
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      – SubjectFull: Prediction models
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              Text: Jun2026
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