Balancing Business and Technical Objectives for Supporting Software Evolution
Bibliographic record
Abstract
Context: Successful software systems continuously evolve to accommodate feature requests of a diverse customer-base. At some point during this evolution, the variety of customer needs and increased system complexity suggests the consideration of a software product line (SPL). Aim: The goal of this research is to support the decision maker facing the enhancement of an evolving software system (ESS) to determine the most appropriate product line design (out of a given set of candidate SPL portfolios) to minimize the technical risk and maximize the business value. Method: The proposed method called OPTESS is aimed at finding an evolution plan for the ESS which optimizes both the given technical and business objectives. Business analysis using a value-based pricing mechanism is applied to a set of initially proposed SPL portfolios (for enhancing the ESS) such that profit is maximized. Technical analysis is applied to the same initially proposed SPL portfolios to minimize the risk of failure of ESS due to implementation of new features. Business and technical analyses improve the performance of solutions for their respective objectives by modifying the feature sets of candidate SPL portfolios. OPTESS helps the decision maker to select a plan for enhancement of ESS by performing trade-off analysis on the economic and technical objectives. Results: The method was initially evaluated by a case study for a set of 9 new candidate features to be added to an open source text editing system called jEdit. OPTESS helped the decision maker to identify 3 non-dominated solutions considered to be of highest preference for decision-making when looking at both technical and economic criteria.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".