Emmanuel Iko-Ojo Simon

2papers

2 Papers

SENov 11, 2023Code
DocGen: Generating Detailed Parameter Docstrings in Python

Vatsal Venkatkrishna, Durga Shree Nagabushanam, Emmanuel Iko-Ojo Simon et al.

Documentation debt hinders the effective utilization of open-source software. Although code summarization tools have been helpful for developers, most would prefer a detailed account of each parameter in a function rather than a high-level summary. However, generating such a summary is too intricate for a single generative model to produce reliably due to the lack of high-quality training data. Thus, we propose a multi-step approach that combines multiple task-specific models, each adept at producing a specific section of a docstring. The combination of these models ensures the inclusion of each section in the final docstring. We compared the results from our approach with existing generative models using both automatic metrics and a human-centred evaluation with 17 participating developers, which proves the superiority of our approach over existing methods.

SEApr 7
A Survey of Algorithm Debt in Machine and Deep Learning Systems: Definition, Smells, and Future Work

Emmanuel Iko-Ojo Simon, Chirath Hettiarachchi, Fatemeh Fard et al.

The adoption of Machine and Deep Learning (ML/DL) technologies introduces maintenance challenges, leading to Technical Debt (TD). Algorithm Debt (AD) is a TD type that impacts the performance and scalability of ML/DL systems. A review of 42 primary studies expanded AD's definition, uncovered its implicit presence, identified its smells, and highlighted future directions. These findings will guide an AD-focused study, enhancing the reliability of ML/DL systems.