Towards Efficient Instrumentation for Reverse-Engineering Object Oriented Software through Static and Dynamic Analyses
Bibliographic record
Abstract
In software engineering, program analysis is usually classified according to static analysis (by analyzing source code) and dynamic analysis (by observing program executions).While static analysis provides inaccurate and imprecise results due to programming language's features (e.g., late binding), dynamic analysis produces more accurate and precise results at runtime at the expense of longer executions to collect traces.One prime mechanism to observe executions in dynamic analysis is to instrument either the code or the binary/byte code.Instrumentation overhead potentially poses a serious threat to the accuracy of the dynamic analysis, especially for time dependent software systems (e.g., real-time software), since it can cause those software systems to go out of synchronization.For instance, in a typical real-time software, the dynamic analysis result is correct if the instrumentation overhead, which is due to gathering dynamic information, does not add to the response time of real-time behaviour to the extent that deadlines may be missed.If a deadline is missed, the dynamic analysis result and the system's output are not accurate.There are two ways to increase accuracy of a dynamic analysis: devising more efficient instrumentation and using a hybrid (static plus dynamic) analysis.A hybrid analysis is a favourable approach to cope with the overhead problem over a purely dynamic analysis.Yet, in the context of reverse engineering source code to produce method calls dynamic and hybrid instrumentations typically lead to large execution traces and consequently large execution overhead.iii This thesis is a step towards efficient and accurate information collection through a hybrid analysis procedure to reverse engineer source code to produce method calls, with the prime objective to reduce instrumentation overhead.To that aim, the first contribution of this thesis is to systematically analyze the contribution to instrumentation overhead of different elements of an existing and promising hybrid solution.Then, a second contribution of the thesis is to suggest an instrumentation optimization process with a range of different designs for those elements to reduce the overhead and select the best one for each element to optimize that solution.The resulting optimized hybrid technique, our third contribution, which potentially produces more accurate instrumentation compared to that hybrid solution for multi-thread software by reducing execution overhead by three quarters, has a reasonable efficiency to reverse engineer programs to produce method calls for multi-threaded software.A final contribution of this thesis is to suggest a set of recommendations for efficient instrumentation.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".