Improving the quality of large-scale database-centric software systems by analyzing database access code
Bibliographic record
Abstract
Due to the emergence of cloud computing and big data applications, modern software systems are becoming more dependent on the underlying database management systems (DBMSs) for data integrity and management. Since DBMSs are very complex and each technology has some implementation-specific differences, DBMSs are usually used as black boxes by software developers, which allow better adaption and abstraction of different database technologies. For example, Object-Relational Mapping (ORM) is one of the most popular database abstraction approaches that developers use. Using ORM, objects in Object-Oriented languages are mapped to records in the DBMS, and object manipulations are automatically translated to SQL queries. Despite ORM's convenience, there exists impedance mismatches between the Object-Oriented paradigm and the relational DBMSs. Such impedance mismatches may result in developers writing inefficiently and/or incorrectly database access code. Thus, this thesis proposes several approaches to improve the quality of database-centric software systems by looking at the application source code. We focus on troubleshooting and detecting inefficient (i.e., performance problems) and incorrect (i.e., functional problems) database accesses in the source code, and we prioritize the detected problems based on severity. Through case studies on large commercial and open source systems, we plan to demonstrate the value of improving the quality of database-centric software systems from a new perspective - helping developers access the database more efficiently and accurately.
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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 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".