Related papers: MultiDimEr: a multi-dimensional bug analyzEr

Context: There is an increase in the investment and development of data-intensive (DI) solutions, systems that manage large amounts of data. Without careful management, this growing investment will also grow associated technical debt (TD).…

With the rapid development of large language models (LLMs), distributed training and inference frameworks like DeepSpeed have become essential for scaling model training and inference across multiple GPUs or nodes. However, the increasing…

In modern software engineering, build systems play the crucial role of facilitating the conversion of source code into software artifacts. Recent research has explored high-level causes of build failures, but has largely overlooked the…

The utilization of third-party open-source libraries is widespread in modern software development. Due to the dependency relationships, vulnerabilities within open-source libraries pose significant security threats to downstream software.…

Monolithic Firmware is widespread. Unsurprisingly, fuzz testing firmware is an active research field with new advances addressing the unique challenges in the domain. However, understanding and evaluating improvements by deriving metrics…

As a representative literate programming platform, Jupyter is widely adopted by developers, data analysts, and researchers for replication, data sharing, documentation, interactive data visualization, and more. Understanding the bugs in the…

Bugs are notoriously challenging: they slow down software users and result in time-consuming investigations for developers. These challenges are exacerbated when bugs must be reported in natural language by users. Indeed, we lack reliable…

Determining whether a configurable software system has a performance bug or it was misconfigured is often challenging. While there are numerous debugging techniques that can support developers in this task, there is limited empirical…

We introduced a new approach to describe Java software as graph, where nodes represent a Java file - called compilation unit (CU) - and an edges represent a relations between them. The software system is characterized by the degree…

Recent advances in neural modeling for bug detection have been very promising. More specifically, using snippets of code to create continuous vectors or \textit{embeddings} has been shown to be very good at method name prediction and…

Hardware complexity continues to strain verification resources, motivating the adoption of machine learning (ML) methods to improve debug efficiency. However, ML-assisted debugging critically depends on diverse and scalable bug datasets,…

Multiple approaches have been proposed to automatically recommend potential developers who can address bug reports. These approaches are typically designed to work for any bug report submitted to any software project. However, we conjecture…

Millions of open-source projects with numerous bug fixes are available in code repositories. This proliferation of software development histories can be leveraged to learn how to fix common programming bugs. To explore such a potential, we…

Fault diagnosis has attracted extensive attention for its importance in the exceedingly fault management framework for cloud virtualization, despite the fact that fault diagnosis becomes more difficult due to the increasing scalability and…

Static analysis is one of the most widely adopted techniques to find software bugs before code is put in production. Designing and implementing effective and efficient static analyses is difficult and requires high expertise, which results…

Programming errors that degrade the performance of systems are widespread, yet there is little tool support for analyzing these bugs. We present a method based on differential performance analysis---we find inputs for which the performance…

This work proposes to put up a tool for diagnosing multi faults based on model using techniques of detection and localization inspired from the community of artificial intelligence and that of automatic. The diagnostic procedure to be…

During the life cycle of software development, developers have to fix different kinds of bugs reported by testers or end users. The efficiency and effectiveness of fixing bugs have a huge impact on the reliability of the software as well as…

A substantial fraction of the time that computational modellers dedicate to developing their models is actually spent trouble-shooting and debugging their code. However, how this process unfolds is seldom spoken about, maybe because it is…

In today's data-driven era, deep learning is vital for processing massive datasets, yet single-device training is constrained by computational and memory limits. Distributed deep learning overcomes these challenges by leveraging multiple…