Feed‐forward and recurrent neural networks for source code informal information analysis
Bibliographic record
Abstract
Abstract Design recovery, which is a part of the reverse engineering process of source code, must supply programmers with all the information they need to fully understand a program or a system. In this paper, a connectionist method that can be used for design recovery in conjunction with more traditional approaches is proposed for analyzing the informal information (comments and mnemonics) in programs. An approach based on artificial neural networks (ANNs) was chosen because of its property of being robust (capable of tolerating noisy inputs), because of its associative memory ability (capable of retrieving a concept given only the context of the input word that originally fired the concept), and because of its generalization power (ability to learn conceptually relevant micro‐features of the domain). The proposed approach uses a combination of top down domain analysis (i.e., the creation of a concept hierarchy by a domain expert, to be used in the construction of the training set) and a bottom up approach (i.e., the analysis of the informal information using ANNs). A preprocessing system that extracts the relevant comments and identifier names and transforms them into an input for the ANNs has been developed. Feed‐forward neural networks (FNNs) and recurrent neural networks (RNNs) were tried. RNN architectures are capable of learning sequences and are able to make use of the word ordering of the sentence. The networks were trained on part of the source code of an existing system and tested on a different portion of the system code. Test results, consisting of coverage and evaluation figures, are presented. They show a remarkably higher accuracy when ANNs, in general, are used as opposed to simple lexical methods. RNNs, in particular, also show higher coverage and accuracy than FNNs. Copyright © 2003 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".