---
title: "Source Code Informal Information Analysis Using Connectionist Models"
authors:
  - "Ettore Merlo"
  - "Ian McAdam"
  - "Renato de Mori"
doi: ""
url: "https://ijcai.org/proceedings/1993/185"
pdf: "https://ijcai.org/proceedings/1993/0185.pdf"
venue: "Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence"
year: 1993
pages: "1339-1344"
track: "Neural Networks"
keywords:
---

# Source Code Informal Information Analysis Using Connectionist Models

Ettore Merlo, Ian McAdam, Renato de Mori

*Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence*, pages 1339-1344.

## Abstract

Design recovery, a part of the reverse engineering process of a program, must supply a programmer with all the information they need to fully understand a program or a system. In this paper, a connectionist method is proposed for analyzing the informal information (comments and mnemonics) in a program that can be used for design recovery in conjunction with more traditional approaches. A connectionist model was chosen because of its properties of being robust (capable of tolerating noisy inputs), it's associative memory ability (capable of retrieving a concept given only the context of the input word that originally ftred the concept), and it's generalization ability (learn conceptually relevant micro-features of the domain). This approach uses a combination of top down domain analysis (the creation of a concept hierarchy by a domain expert, to be used in the formation of the training set) and a bottom up approach (the analysis of the informal information using the network).