An exploratory study of the evolution of communicated information about the execution of large software systems
Bibliographic record
Abstract
SUMMARY Substantial research in software engineering focuses on understanding the dynamic nature of software systems in order to improve software maintenance and program comprehension. This research typically makes use of automated instrumentation and profiling techniques after the fact, that is, without considering domain knowledge. In this paper, we examine another source of dynamic information that is generated from statements that have been inserted into the code base during development to draw the system administrators' attention to important run‐time phenomena. We call this source communicated information (CI). Examples of CI include execution logs and system events. The availability of CI has sparked the development of an ecosystem of Log Processing Apps ( LPA s) that surround the software system under analysis to monitor and document various run‐time constraints. The dependence of LPAs on the timeliness, accuracy and granularity of the CI means that it is important to understand the nature of CI and how it evolves over time, both qualitatively and quantitatively. Yet, to our knowledge, little empirical analysis has been performed on CI and its evolution. In a case study on two large open source and one industrial software systems, we explore the evolution of CI by mining the execution logs of these systems and the logging statements in the source code. Our study illustrates the need for better traceability between CI and the LPAs that analyze the CI. In particular, we find that the CI changes at a high rate across versions, which could lead to fragile LPAs. We found that up to 70% of these changes could have been avoided and the impact of 15% to 80% of the changes can be controlled through the use of robust analysis techniques by LPAs. We also found that LPAs that track implementation‐level CI (e.g. performance analysis) and the LPAs that monitor error messages (system health monitoring) are more fragile than LPAs that track domain‐level CI (e.g. workload modelling), because the latter CI tends to be long‐lived. Copyright © 2013 John Wiley & Sons, Ltd.
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 | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".