This paper proposes a decentralized software version control system leveraging blockchain technology. Traditional version control systems suffer from central points of failure, lack of transparency, and limited traceability. This system addresses these shortcomings by utilizing a blockchain to immutably record and manage software version information. The core mechanism involves hashing software versions and storing these hashes on a blockchain, ensuring data integrity and providing a transparent, auditable trail. This approach enhances security, improves transparency, and offers enhanced traceability compared to centralized solutions. The system utilizes a distributed consensus mechanism to maintain blockchain integrity, mitigating the risks associated with a single point of failure. This research explores the feasibility and potential benefits of applying blockchain technology to software version control, representing a novel approach to managing software evolution.
This paper proposes a novel approach to software version control leveraging the inherent properties of blockchain technology. Traditional version control systems suffer from centralized vulnerabilities and single points of failure, leading to potential data loss and compromised integrity. This system addresses these limitations by utilizing blockchain's immutable ledger and distributed storage capabilities. Each software code version is recorded as a transaction on the blockchain, secured by a consensus mechanism. This ensures a complete and verifiable history of code changes, dramatically enhancing security, reliability, and transparency compared to conventional methods. The core claim of this work is that the combination of blockchain's features provides a significantly more robust and trustworthy software version control solution. The system's design incorporates key mechanisms such as transaction hashing, smart contracts for version management, and a distributed consensus protocol to guarantee data integrity and consistency. The resulting architecture offers a compelling alternative for organizations seeking a secure and resilient software development environment.
Abstract Mutation testing is widely used to assess the adequacy of test suite; however, its practical adoption is constrained by the persistent problem of equivalent mutants, i.e., mutants whose observable behavior is indistinguishable from the original program and therefore cannot be killed by any test case. Prior studies report that identifying equivalent mutants often requires substantial manual effort. Meanwhile, fuzzing is increasingly used in real-world systems, especially security-critical software. However, conventional fuzzing oracles typically detect only crash-like failures, thereby missing many behavioral changes introduced by mutants. In this paper, we investigate differential fuzzing as a practical, language-agnostic approach to support mutant classification, focusing on identifying (and confirming) equivalent mutants. We conduct a multi-project study across Bitcoin Core (C++), OpenSSL (C), LND (Go), and Arrow (Python), mutating six real functions with 1,090 valid mutants generated by the universalmutator. We compare unit/functional testing, seed-corpus-only fuzzing, time-bounded fuzzing, and two differential fuzzing configurations (seed-corpus-only and time-bounded). Our results show that conventional fuzzing yields the lowest mutation scores, while time-bounded differential fuzzing (5 minutes per mutant) achieves 98–100\% mutation score in five of six targets, and exposes cases where seed corpora contain valuable test inputs not covered by unit/functional suites. We further analyze runtime variability and observe log-normal behavior in difficult cases, providing practical guidance on stopping criteria (time/execution budgets), as well as evidence that dictionaries and parallel fuzzing can significantly improve effectiveness. Overall, our findings indicate that differential fuzzing is simple to implement and can classify mutants efficiently in practice, while also producing actionable artifacts (seed corpus inputs) to strengthen traditional test suites.
Abstract This paper presents a novel, vendor-agnostic stateful orchestration architecture designed to mitigate systemic data latency and transactional asymmetry within integrated enterprise-level logistics frameworks. In multi-enterprise distributed environments, the decoupling of decentralized physical Warehouse Management Systems (WMS) from centralized Enterprise Resource Planning (ERP) database cores introduces severe synchronization boundary failures. We systematically analyze three critical points of operational vulnerability: serialization asymmetry in distributed tracking, atomic transaction failures during partial outbound executions, and inventory record propagation delay within reverse logistics matrices. To resolve these vulnerabilities, we introduce the State-Aware, Automated Alignment, Integration Integrity, and Logistics Cost Optimization (SAIL) framework. By transitioning integration middleware from stateless message routing pipes to active, state-retaining orchestration layers, the SAIL framework leverages an automated Heuristic Validation Buffer (HVB), an asynchronous Dual-Handshake Programmatic Lock protocol, and multi-variable cost-weight heuristic models. Field evaluation validates that the proposed architecture eliminates up to 90% of manual inventory remediation transactions while maintaining strict end-to-end ledger integrity across completely decoupled data systems.