Bibliographic record
Abstract
Functionality of software products often does not match user needs and expectations. The closed set-up of systems and information is replaced by wide access to data of users and competitor products. This shift offers completely new opportunities to approach requirements elicitation and subsequent planning of software functionality. This is, in particular true for app store markets. App stores are markets for many small sized software products which provide an open platform for users to provide feedback on using apps. Moreover, the functionality and status of similar software products can be retrieved. While this is a competitive risk, it is at the same time an opportunity.In this paper, we envision a new release planning approach that leverages the new opportunities for decision making. We propose a new model using bi-criterion integer programming. We make suggestions for optimized super app functionality that are based on two key aspects: (i) the estimated value of features, and (ii) the cohesiveness between newly added features and cohesiveness between existing and the features to be added. The information on these attributes comes from reasoning on feature composition of existing similar apps. The approach is applicable to the development of new product releases as well as to the creation of completely new apps. We illustrate the applicability of our model by a small example and outline directions for future research.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".