MoveMethod is a hallmark refactoring. Despite a plethora of research tools that recommend which methods to move and where, these recommendations do not align with how expert developers perform Movemethod. Given the extensive training of Large Language Models and their reliance upon naturalness of code, they should expertly recommend which methods are misplaced in a given class and which classes are better hosts. Our formative study of 2016 LLM recommendations revealed that LLMs give expert suggestions, yet they are unreliable: up to 80 % of the suggestions are hallucinations. We introduce the first LLM fully powered assistant for MoveMethod refactoring that automates its whole end-to-end lifecycle, from recommendation to execution. We designed novel solutions that automatically filter LLM hallucinations using static analysis from IDEs and a novel workflow that requires LLMs to be self-consistent, critique, and rank refactoring suggestions. As MoveMethod refactoring requires global, project-level reasoning, we solved the limited context size of LLMs by employing refactoring-aware retrieval augment generation (RAG). Our approach, MM-assist, synergistically combines the strengths of the LLM, IDE, static analysis, and semantic relevance. In our thorough, multi-methodology empirical evaluation, we compare MM-assist with the previous state-of-the-art approaches. MMASSIST significantly outperforms them: (i) on a benchmark widely used by other researchers, our Recall@1 and Recall@3 show a$1.7 x$improvement; (ii) on a corpus of 210 recent refactorings from Open-source software, our Recall rates improve by at least$\mathbf{2. 4 x}$. Lastly, we conducted a user study with$\mathbf{3 0}$experienced participants who used MM-ASSIST to refactor their own code for one week. They rated$\mathbf{8 2. 8 \%}$of MM-aSSIST recommendations positively. This shows that MM-ASSIST is both effective and useful.

A new paradigm for digital identity management called Self-Sovereign Identity (SSI) has emerged to offer users more control over their identity data. An important component of SSI is a wallet that stores cryptographic keys and other identity data. Secure backup and recovery methods for data stored in such wallets are a crucial feature for its wide-scale adoption, however, such a feature is lacking or implemented casually in the existing SSI wallets. In this research, we propose a secure backup and storage of SSI wallets using a novel technology called Solid Pod. Solid Pod is an emerging technology that enables users to securely store data online. Towards this aim, we present the architecture, based on a threat model and requirement analysis, of the proposed approach. We also discuss its implementation details, outline a number of protocol flows highlighting different use-cases and analyse its security, advantages and limitations.

Patch prioritization ranks candidate patches based on their likelihood of being correct.The fixing ingredients that are more likely to be the fix for a bug, share a high contextual similarity.A recent study shows that combining both syntactic and semantic similarity for capturing the contextual similarity, can do better in prioritizing patches.In this study, we evaluate the impact of combining the syntactic and semantic features on patch prioritization using the Insertion mutation operators.This study inspects the result of different combinations of syntactic and semantic features on patch prioritization.As a pilot study, the approach uses genealogical similarity to measure the semantic similarity and normalized longest common subsequence, normalized edit distance, cosine similarity, and Jaccard similarity index to capture the syntactic similarity.It also considers Anti-Pattern to filter out the incorrect plausible patches.The combination of both syntactic and semantic similarity can reduce the search space to a great extent.Also, the approach generates fixes for the bugs before the incorrect plausible one.We evaluate the techniques on the IntroClassJava benchmark using Insertion mutation operators and successfully generate fixes for 6 bugs before the incorrect plausible one.So, considering the previous study, the approach of combining syntactic and semantic similarity can able to solve a total number of 25 bugs from the benchmark, and to the best of our knowledge, it is the highest number of bug solved than any other approach.The correctness of the generated fixes are further checked using the publicly available results of CapGen and thus for the generated fixes, the approach achieves a precision of 100%.