QUANTIFYING THE IMPACT OF AI-POWERED CODING TOOLS ON SOFTWARE DEVELOPER PRODUCTIVITY
Budget: $30 – $250 USD
I have a cleaned set of user-behaviour data and the goal is to turn it into a full, publish-ready manuscript for an SCI/SSCI indexed engineering journal that sits squarely in the AI–MIS space. The work must be grounded in current, peer-reviewed sources, demonstrate a transparent methodology, and end with reproducible, hands-on results that withstand the journal’s review process.
What I will supply
• base topic and title: QUANTIFYING THE IMPACT OF AI-POWERED CODING TOOLS ON SOFTWARE DEVELOPER PRODUCTIVITY
• any target-journal formatting guidelines once we agree on the outlet
What you will deliver
• a 6 000–8 000-word manuscript (abstract to references) in the journal’s template
• rigorous literature review with serious, up-to-date citations
• clearly documented AI models or algorithms applied to the dataset, including code and parameter settings
• statistical validation, visualisations, and a discussion that links findings to MIS theory
• all figures, tables, and supplementary files in editable form
• a short cover letter to the editor highlighting originality and fit
Acceptance criteria
1. Results are fully reproducible from the supplied code and data.
2. Similarity index ≤ 15 % on iThenticate or equivalent.
3. Reference list follows the exact style required by the target journal.
4. Manuscript is returned by the mutually agreed deadline, ready for submission with no major revisions requested at first check.
Python, R, or MATLAB are fine for the analysis—use whichever you are most efficient with, as long as the workflow is clearly annotated. I’m responsive to questions and can approve milestones quickly so we stay on schedule.
What I will supply
• base topic and title: QUANTIFYING THE IMPACT OF AI-POWERED CODING TOOLS ON SOFTWARE DEVELOPER PRODUCTIVITY
• any target-journal formatting guidelines once we agree on the outlet
What you will deliver
• a 6 000–8 000-word manuscript (abstract to references) in the journal’s template
• rigorous literature review with serious, up-to-date citations
• clearly documented AI models or algorithms applied to the dataset, including code and parameter settings
• statistical validation, visualisations, and a discussion that links findings to MIS theory
• all figures, tables, and supplementary files in editable form
• a short cover letter to the editor highlighting originality and fit
Acceptance criteria
1. Results are fully reproducible from the supplied code and data.
2. Similarity index ≤ 15 % on iThenticate or equivalent.
3. Reference list follows the exact style required by the target journal.
4. Manuscript is returned by the mutually agreed deadline, ready for submission with no major revisions requested at first check.
Python, R, or MATLAB are fine for the analysis—use whichever you are most efficient with, as long as the workflow is clearly annotated. I’m responsive to questions and can approve milestones quickly so we stay on schedule.