Revolutionizing Code Optimization: A Deep Dive into AI-Powered Tools for Software Development
Bibliographic record
Abstract
The incorporation of AI technologies into software development processes is a potential game changer for code optimization, helping developers to create portable, maintainable, and efficient code. We examined five advanced AI-integrated code optimization tools, namely GPT-4, Claude, Gemini, Llama, and Github Copilot. Each was evaluated on a custom dataset with code templates for several tasks such as: a counting task, a prefix computation task, a pattern matching task, a digit verification task, and a uniqueness guaranteeing task. These tools are particularly effective for more complex and multifaceted requirements, as the assessment relies on important parameters: execution time through optimization of algorithms and data structures, cheap alternatives for memory consumption, energy savings for mobile and embedded system devices, and coping mechanisms for error and fault tolerance. This meticulous research shows the merits and demerits of each tool, providing information to researchers and developers willing to improve the software system performance and reliability with the aid of AI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".