175,449 research outputs found
Synthetic Dataset Generation with Itemset-Based Generative Models
This paper proposes three different data generators, tailored to
transactional datasets, based on existing itemset-based generative models. All
these generators are intuitive and easy to implement and show satisfactory
performance. The quality of each generator is assessed by means of three
different methods that capture how well the original dataset structure is
preserved.Comment: IEEE International Symposium on Software Reliability Engineering
Workshops (ISSREW@RDSA 2019), Oct 201
Empirical Notes on the Interaction Between Continuous Kernel Fuzzing and Development
Fuzzing has been studied and applied ever since the 1990s. Automated and
continuous fuzzing has recently been applied also to open source software
projects, including the Linux and BSD kernels. This paper concentrates on the
practical aspects of continuous kernel fuzzing in four open source kernels.
According to the results, there are over 800 unresolved crashes reported for
the four kernels by the syzkaller/syzbot framework. Many of these have been
reported relatively long ago. Interestingly, fuzzing-induced bugs have been
resolved in the BSD kernels more rapidly. Furthermore, assertions and debug
checks, use-after-frees, and general protection faults account for the majority
of bug types in the Linux kernel. About 23% of the fixed bugs in the Linux
kernel have either went through code review or additional testing. Finally,
only code churn provides a weak statistical signal for explaining the
associated bug fixing times in the Linux kernel.Comment: The 4th IEEE International Workshop on Reliability and Security Data
Analysis (RSDA), 2019 IEEE International Symposium on Software Reliability
Engineering Workshops (ISSREW), Berlin, IEE
Security Assessment and Hardening of Fog Computing Systems
In recent years, there has been a shift in computing architectures, moving
away from centralized cloud computing towards decentralized edge and fog
computing. This shift is driven by factors such as the increasing volume of
data generated at the edge, the growing demand for real-time processing and
low-latency applications, and the need for improved privacy and data locality.
Although this new paradigm offers numerous advantages, it also introduces
significant security and reliability challenges. This paper aims to review the
architectures and technologies employed in fog computing and identify
opportunities for developing novel security assessment and security hardening
techniques. These techniques include secure configuration and debloating to
enhance the security of middleware, testing techniques to assess secure
communication mechanisms, and automated rehosting to speed up the security
testing of embedded firmware.Comment: 4 pages, Accepted for publication at The 34th IEEE International
Symposium on Software Reliability Engineering Workshops (ISSREW
Enhancing Failure Propagation Analysis in Cloud Computing Systems
In order to plan for failure recovery, the designers of cloud systems need to
understand how their system can potentially fail. Unfortunately, analyzing the
failure behavior of such systems can be very difficult and time-consuming, due
to the large volume of events, non-determinism, and reuse of third-party
components. To address these issues, we propose a novel approach that joins
fault injection with anomaly detection to identify the symptoms of failures. We
evaluated the proposed approach in the context of the OpenStack cloud computing
platform. We show that our model can significantly improve the accuracy of
failure analysis in terms of false positives and negatives, with a low
computational cost.Comment: 12 pages, The 30th International Symposium on Software Reliability
Engineering (ISSRE 2019
Dependability Evaluation of Middleware Technology for Large-scale Distributed Caching
Distributed caching systems (e.g., Memcached) are widely used by service
providers to satisfy accesses by millions of concurrent clients. Given their
large-scale, modern distributed systems rely on a middleware layer to manage
caching nodes, to make applications easier to develop, and to apply load
balancing and replication strategies. In this work, we performed a
dependability evaluation of three popular middleware platforms, namely
Twemproxy by Twitter, Mcrouter by Facebook, and Dynomite by Netflix, to assess
availability and performance under faults, including failures of Memcached
nodes and congestion due to unbalanced workloads and network link bandwidth
bottlenecks. We point out the different availability and performance trade-offs
achieved by the three platforms, and scenarios in which few faulty components
cause cascading failures of the whole distributed system.Comment: 2020 IEEE 31st International Symposium on Software Reliability
Engineering (ISSRE 2020
LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning (Practical Experience Report)
The automation of code review activities, a long-standing pursuit in software
engineering, has been primarily addressed by numerous domain-specific
pre-trained models. Despite their success, these models frequently demand
extensive resources for pre-training from scratch. In contrast, Large Language
Models (LLMs) provide an intriguing alternative, given their remarkable
capabilities when supplemented with domain-specific knowledge. However, their
potential for automating code review tasks remains largely unexplored.
In response to this research gap, we present LLaMA-Reviewer, an innovative
framework that leverages the capabilities of LLaMA, a popular LLM, in the realm
of code review. Mindful of resource constraints, this framework employs
parameter-efficient fine-tuning (PEFT) methods, delivering high performance
while using less than 1% of trainable parameters.
An extensive evaluation of LLaMA-Reviewer is conducted on two diverse,
publicly available datasets. Notably, even with the smallest LLaMA base model
consisting of 6.7B parameters and a limited number of tuning epochs,
LLaMA-Reviewer equals the performance of existing code-review-focused models.
The ablation experiments provide insights into the influence of various
fine-tuning process components, including input representation, instruction
tuning, and different PEFT methods. To foster continuous progress in this
field, the code and all PEFT-weight plugins have been made open-source.Comment: Accepted to the 34th IEEE International Symposium on Software
Reliability Engineering (ISSRE 2023
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