Challenges of Implementing Software Variability in Eclipse OMR: An Interview Study
Bibliographic record
Abstract
Software variability is the ability of a software system to be customized or configured for a particular context. In this paper, we discuss our experience investigating software variability implementation challenges in practice. Eclipse OMR, developed by IBM, is a set of highly configurable C++ components for building language runtimes; it supports multiple programming languages and target architectures. We conduct an interview study with 6 Eclipse OMR developers and identify 8 challenges incurred by the existing variability implementation, and 3 constraints that need to be taken into account for any reengineering effort. We discuss these challenges and investigate the literature and existing open-source systems for potential solutions. We contribute a solution for one of the challenges, namely adding variability to enumerations and arrays. We also share our experiences and lessons learned working with a large-scale highly configurable industry project. For example, we found that the "latest and greatest" research solutions may not always be favoured by developers due to small practical considerations such as build dependencies, or even C++ version constraints.
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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 | 0.034 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".