What are Practitioners Asking about Requirements Engineering? An Exploratory Analysis of Social Q&A Sites
Bibliographic record
Abstract
Requirements Engineering (RE) and all its underlying activities, such as requirements identification, evolution, validation, communication, and management, are still the key factors in successful product development. Therefore, proper implementation of this process is necessary to obtain a quality product. A better understanding of the most challenging RE-related topics for practitioners will greatly help to identify the areas of RE that may require extra attention by researchers and project managers. However, there has been very little experimental work towards identifying a practitioner's needs on the implementation and understanding of RE activities and tasks. Therefore, in this paper, we use data from popular social Q&A sites (i.e. Stack Overflow, Programmers Stack Exchange, Project Management Stack Exchange, and Quora), and analyze 4,553 questions and answers to examine what requirements engineers' needs are and what they ask about. To this end, we applied Latent Dirichlet Allocation-based (LDA) topic models and statistical analysis to explore the main related topics to RE. Our findings show that software practitioners are asking about requirements communication, evolution, validation, and modelling. Furthermore, we determined common RE challenges and issues, identified the main types of questions practitioners ask (i.e. what, how, why, when, where, and RE domain), and categorized these questions based on various aspects of software products (e.g. functionality, quality, and release planning). Our findings help highlight the challenges facing requirements engineers that require more attention from the software engineering - specifically requirements and product engineering - research communities in the future and establish a novel approach for analyzing the content of social Q&A websites.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".