Registered user since Tue 18 Jan 2022
Contributions
NIER Track
Thu 13 Oct 2022 10:40 - 10:50 at Gold A - Technical Session 24 - Human Aspects Chair(s): Silvia AbrahãoThe logic behind design decisions, called design rationale, is very valuable. In the past, researchers have tried to automatically extract and exploit this information, but prior techniques are only applicable to specific contexts and there is insufficient progress on an end-to-end rationale information extraction pipeline. Here we outline a path towards such a pipeline that leverages several Machine Learning (ML) and Natural Language Processing (NLP) techniques. Our proposed context-independent approach, called Kantara, produces a knowledge graph representation of decisions and of their rationales, which considers their historical evolution and traceability. We also propose inconsistency checking mechanisms to ensure the correctness of the extracted information and the coherence of the development process. We conducted a preliminary evaluation of our proposed approach on a small example sourced from the Linux Kernel, which shows promising results.
Pre-printDoctoral Symposium
Mon 10 Oct 2022 11:30 - 12:00 at Ambassador A - Session 2 - AI & Software EngineeringSoftware developers often have to make many decisions. The underlying logic behind these decisions, also called design rationale, represents beneficial and valuable information. In the past, researchers have tried to automatically extract and exploit this information, however, prior techniques are only applicable to specific contexts and there is insufficient progress on an automated end-to-end rationale extraction and management system. In this research project, we propose to use Natural Language Processing (NLP) and Machine Learning (ML) techniques to create a system for the automated extraction, structuring and management of design rationale. This system would support and ensure the consistency and the coherence of the development process.
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