Enhancing Software Evolution Requirements Engineering Based on User Feedback
Bibliographic record
Abstract
End-user feedback has an essential role in the requirement’s identification, prioritization, and management of the software evolution process. Several approaches are proposed for utilizing user-pushed feedback collected from social media, forums, and review systems. The collected feedback via the online channels contains a variety of information. Thus, the researchers proposed analytical approaches to classify feedback according to the data it holds. Still, recent results indicate that no single classifier works best for all feedback types and information sources. Also, online feedback does not have a direct mapping to the requirements, and it does not contain user context data. This causes wasting in developers’ effort in understanding and analyzing feedback. On the other hand, online feedback cannot be used to explore user satisfaction and acceptance of the implemented and planned requirements. Likewise, the developer cannot collect feedback from a specific segment of the users. To overcome the deficiency of online feedback, this paper proposes a novel approach that utilizes pulling feedback from users while using the software. The proposed approach consists of a model and process for structuring feedback requests, linking feedback to the requirements, embedding feedback with the user context information, specifying the target audience for the feedback request, analyzing collected feedback depending on predefined interpretation rules, which provide insights that support developers in release planning. The feedback request model and process are implemented by a tool called FeatureEcho which was evaluated in a software company by conducting a case study for upgrading a governmental internet portal. The results indicate that FeatureEcho is a valuable step towards increasing the understanding of the end-users needs which supports the decision-making procedure of software evolution.
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 | 0.018 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".