Assessing Semantic Frames to Support Program Comprehension Activities

TL;DR

This paper investigates the use of semantic frame parsing, a linguistic technique, to improve understanding of natural language in software engineering artifacts, proposing a tailored approach called SEFrame.

Contribution

It evaluates the applicability of semantic frame parsing for program comprehension and introduces SEFrame, a tailored method for better natural language understanding in software engineering.

Findings

SEFrame achieves 73-74% correctness in parsing tasks.

It can parse diverse software artifacts effectively.

The approach enhances understanding of developer-related natural language texts.

Abstract

Software developers often rely on natural language text that appears in software engineering artifacts to access critical information as they build and work on software systems. For example, developers access requirements documents to understand what to build, comments in source code to understand design decisions, answers to questions on Q&A sites to understand APIs, and so on. To aid software developers in accessing and using this natural language information, software engineering researchers often use techniques from natural language processing. In this paper, we explore whether frame semantics, a general linguistic approach, which has been used on requirements text, can also help address problems that occur when applying lexicon analysis based techniques to text associated with program comprehension activities. We assess the applicability of generic semantic frame parsing for this purpose, and based on the results, we propose SEFrame to tailor semantic frame parsing for program comprehension uses. We evaluate the correctness and robustness of the approach finding that SEFrame is correct in between 73% and 74% of the cases and that it can parse text from a variety of software artifacts used to support program comprehension. We describe how this approach could be used to enhance existing approaches to identify meaning on intention from software engineering texts.