Automatic Classification of User Requirements from Online Feedback - A Replication Study
Bibliographic record
Abstract
Natural language processing (NLP) techniques have been widely applied in the requirements engineering (RE) field to support tasks such as classification and ambiguity detection. Although RE research is rooted in empirical investigation, it has paid limited attention to the replication of NLP for RE (NLP4RE) studies. Additionally, the rapidly advancing realm of NLP is creating new opportunities for efficient, machine-assisted workflow applications, which can bring new perspectives and results to the forefront. Thus, in this study, we replicate and extend a previous NLP4RE study (baseline), "Classifying User Requirements from Online Feedback in Small Dataset Environments using Deep Learning", which evaluated different deep learning models for requirement classification from user reviews. In this study, we reproduced the original results using the publicly released source code, thereby helping to strengthen the external validity of the baseline study. We then extended the baseline setup by evaluating the model’s performance on an external (new) dataset and comparing the results to a GPT-4o zero-shot classifier. Furthermore, we prepared the replication study ID-card for the baseline study, which is an important aspect to evaluate replication readiness.The results showed diverse reproducibility levels across different models, with Naive Bayes demonstrating perfect reproducibility. In contrast, BERT and other models showed mixed results. Our findings also revealed that baseline deep learning models, BERT and ELMo, exhibited good generalization capabilities on an external dataset, and the GPT-4o model showed performance comparable to traditional baseline machine learning models. Additionally, our assessment of the replication study ID-card confirmed the replication readiness of the baseline study; however, the missing environment setup files would have further enhanced the readiness. We include this missing information in our replication package and provide the replication study ID-card for our study to further encourage and support the replication of our study.
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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 | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".