Correlating Features and Code by Dynamic and Semantic Analysis

TL;DR

This paper introduces a novel method combining semantic and dynamic analysis to locate and understand functional features in code by analyzing execution traces and applying topic modeling.

Contribution

The paper presents a new approach that integrates dynamic execution traces with semantic analysis to identify and relate features to code, enhancing software comprehension.

Findings

Effective in locating features in code

Uses LDA for semantic feature extraction

Provides insights into feature-code relationships

Abstract

One major problem in maintaining a software system is to understand how many functional features in the system and how these features are implemented. In this paper a novel approach for locating features in code by semantic and dynamic analysis is proposed. The method process consists of three steps: The first uses the execution traces as text corpus and the method calls involved in the traces as terms of document. The second ranks the method calls in order to filter out omnipresent methods by setting a threshold. And the third step treats feature-traces as first class entities and extracts identifiers from the rest method source code and a trace-by-identifier matrix is generated. Then a semantic analysis model-LDA is applied on the matrix to extract topics, which act as functional features. Through building several corresponding matrices, the relations between features and code can be obtained for comprehending the system functional intents. A case study is presented and the execution results of this approach can be used to guide future research.