PROSPECTS OF ARTIFICIAL SOFTWARE ENGINEERING

The merging of AI and software engineering marks a defining moment: intelligent systems now move beyond simple code completion or test automation to become active partners in each phase of development. We term this “Artificial Software Engineering,” a collaborative framework where human ingenuity and machine intelligence co–author software—from early prototypes and code generation to debugging and architectural design (Menzies et al., 2019). By looking at platforms like Devin AI and GitHub Copilot, we see clear benefits—faster iterations, deeper error detection—but also face new challenges around trust, ownership, legal responsibility, and maintaining AI‐influenced code over time. Rather than treating automation as an end goal, we argue that this emerging discipline demands fresh thinking about ethics, team dynamics, and design practices. Ultimately, the most successful software.

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Publication Details

Journal
Problems of Information Technology
Published
2025-06-30
DOI
https://doi.org/10.25045/jpit.v16.i2.06
Primary Topic
Advanced Data Processing Techniques
Type
article
Field-Weighted Citation Impact
0.00
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PROSPECTS OF ARTIFICIAL SOFTWARE ENGINEERING

Zafar Jafarov, Atif Namazov, S.E. Aliyeva, Javid Abbasli et al.
Problems of Information Technology
Advanced Data Processing Techniques
article

PROSPECTS OF ARTIFICIAL SOFTWARE ENGINEERING

Zafar Jafarov, Atif Namazov, S.E. Aliyeva, Javid Abbasli, Khalid Nazarov
article en

Abstract

The merging of AI and software engineering marks a defining moment: intelligent systems now move beyond simple code completion or test automation to become active partners in each phase of development. We term this “Artificial Software Engineering,” a collaborative framework where human ingenuity and machine intelligence co–author software—from early prototypes and code generation to debugging and architectural design (Menzies et al., 2019). By looking at platforms like Devin AI and GitHub Copilot, we see clear benefits—faster iterations, deeper error detection—but also face new challenges around trust, ownership, legal responsibility, and maintaining AI‐influenced code over time. Rather than treating automation as an end goal, we argue that this emerging discipline demands fresh thinking about ethics, team dynamics, and design practices. Ultimately, the most successful software.

Problems of Information TechnologyVol. 16(2)
Openalex Percentile: Top 78%
Advanced Data Processing Techniques
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