UML-Driven Software Performance Engineering
Bibliographic record
Abstract
Performance is critical to the success of every software system. As a sub-area of software engineering, Software Performance Engineering (SPE) is a systematic and quantitative discipline to construct software systems that meet performance objectives. A family of SPE approaches that has become popular in the last decade is SPE based on models developed using the Unified Modeling Language (UML), referred to as UML-Driven Software Performance Engineering (UML-SPE). This particular research area has emerged and grown since late 1990s when the UML was proposed. More than 100 papers have been published so far in this area. As this research area matures and the number of related papers increases, it is important to systematically summarize and categorize the current state-of-the-art and to provide an overview of the trends in this specialized field. The authors systematically map the body of knowledge related to UML-SPE through a Systematic Mapping (SM) study. As part of this study, they pose two sets of research questions, define selection and exclusion criteria, and systematically develop and refine a systematic map (classification schema). In addition, the authors conduct bibliometric, demographic, and trend analysis of the included papers. The study pool includes a set of 90 papers (from 114 identified papers) published in the area of UML-SPE between 1998 and 2011. The authors derive the trends in terms of types of papers, types of SPE activities, and types of evaluations. They also report the demographics and bibliometrics trends in this domain and discuss the emerging trends in UML-SPE and the implications for researchers and practitioners in this area.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".