CodeLedger: a Blockchain-Based Framework for Secure Software Version Control
Bibliographic record
Abstract
Version control systems (VCS) are an integral part of software development, allowing teams to track changes, manage code history, and collaborate efficiently. However, traditional VCS solutions, such as Git and SVN, face several security and integrity challenges, including centralized dependency, unauthorized modifications, and data tampering. These issues highlight the need for a more secure and transparent approach to version control. This paper introduces CodeLedger, a blockchain-based software versioning system that leverages decentralized ledger technology (DLT) to ensure tamper-proof commit histories, secure collaboration, and transparent auditing. By integrating cryptographic hashing, smart contracts, and distributed consensus mechanisms, CodeLedger eliminates single points of failure and enhances the integrity of code repositories. To evaluate the effectiveness of blockchain-based version control, this study compares CodeLedger with traditional VCS in terms of security, performance, and scalability. The proposed system is tested using various experimental setups, and results indicate that despite the storage and computational overhead of blockchain, CodeLedger provides significantly improved security and data immutability. Additionally, the paper presents a detailed survey of few research papers on blockchain applications in software engineering and discusses the potential future improvements of decentralized version control. Experimental results, performance metrics, and security evaluations are included to validate the practicality of CodeLedger in real-world development environments.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".