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Identifying, localizing, and resolving bugs in software engineering is challenging and costly. Approaches to resolve software bugs range from Large Language Model (LLM) code analysis and repair, and automated code repair technology that aims to alleviate the technical burden of difficult to solve bugs. We propose RAGFix, which enhances LLM’s capabilities for bug localization and code repair using Retrieval Augmented Generation (RAG) based on dynamically collected Stack Overflow posts. These posts are searchable via a Question and Answer Knowledge Graph (KGQA). We evaluate our method on the HumanEvalFix benchmark for Python using relevant closed and open-source models. Our approach facilitates error resolution in Python coding problems by creating a searchable, embedded knowledge graph representation of bug and solution information from Stack Overflow, interlinking bugs, and solutions through semi-supervised graph construction methods. We use cosine similarity on embeddings based on LLM-synthesized summaries and algorithmic features describing the coding problem and potential solution to find relevant results that improve LLM in-context performance. Our results indicate that our system enhances small open-source models’ ability to effectively repair code, particularly where these models have less parametric knowledge about relevant coding problems and can leverage nonparametric knowledge to provide accurate, actionable fixes. 

Deep neural networks (DNNs) are increasingly used in critical applications like autonomous vehicles and medical diagnosis, where accuracy and reliability are crucial. However, debugging DNNs is challenging and expensive, often leading to unpredictable behavior and performance issues. Identifying and diagnosing bugs in DNNs is difficult due to complex and obscure failure symptoms, which are data-driven and compute-intensive. To address this, we propose TransBug a framework that combines transformer models for feature extraction with deep learning models for classification to detect and diagnose bugs in DNNs. We employ a pre-trained transformer model, which has been trained in programming languages, to extract semantic features from both faulty and correct DNN models. We then use these extracted features in a separate deep-learning model to determine whether the code contains bugs. If a bug is detected, the model further classifies the type of bug. By leveraging the powerful feature extraction capabilities of transformers, we capture relevant characteristics from the code, which are then used by a deep learning model to identify and classify various types of bugs. This combination of transformer-based feature extraction and deep learning classification allows our method to accurately link bug symptoms to their causes, enabling developers to take targeted corrective actions. Empirical results show that the TransBug shows an accuracy of 81% for binary classification and 91% for classifying bug types. 

We revisit the problem of building static hash tables on the GPU and present an efficient implementation of bucketed hash tables. By decoupling the probing scheme from the hash table in-memory representation, we offer an implementation where the number of probes and the bucket size are the only factors limiting performance. Our analysis sweeps through the hash table parameter space for two probing schemes: cuckoo and iceberg hashing. We show that a bucketed cuckoo hash table (BCHT) that uses three hash functions outperforms alternative methods that use iceberg hashing and a cuckoo hash table that uses a bucket size of one. At load factors as high as 0.99, BCHT enjoys an average probe count of 1.43 during insertion. Using three hash functions only, positive and negative queries require at most 1.39 and 2.8 average probes per key, respectively. 

Internal fragments generated by top-down mass spectrometry can increase sequence coverage, localize disulfide bonds, and determine disulfide connectivity of disulfide-containing proteins.