290 research outputs found
Unsuspected axillary lymph node metastasis of nasopharyngeal and cervical cancer on 18FDG PET/CT: a case report
Axillary lymph node metastasis (ALNM) from cancer except breast cancer is rare. Whole body 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) that simultaneously offers anatomic and metabolic information is widely used and has become an effective modality in many clinical fields, especially oncology, and also may detect an unexpected metastasis. We report two cases of ALMN of nasopharyngeal and cervical cancer that was detected on whole body PET/CT
Towards Objective-Tailored Genetic Improvement Through Large Language Models
While Genetic Improvement (GI) is a useful paradigm to improve functional and
nonfunctional aspects of software, existing techniques tended to use the same
set of mutation operators for differing objectives, due to the difficulty of
writing custom mutation operators. In this work, we suggest that Large Language
Models (LLMs) can be used to generate objective-tailored mutants, expanding the
possibilities of software optimizations that GI can perform. We further argue
that LLMs and the GI process can benefit from the strengths of one another, and
present a simple example demonstrating that LLMs can both improve the
effectiveness of the GI optimization process, while also benefiting from the
evaluation steps of GI. As a result, we believe that the combination of LLMs
and GI has the capability to significantly aid developers in optimizing their
software.Comment: Accepted to the 12th International Workshop on Genetic Improvemen
Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction
Many automated test generation techniques have been developed to aid
developers with writing tests. To facilitate full automation, most existing
techniques aim to either increase coverage, or generate exploratory inputs.
However, existing test generation techniques largely fall short of achieving
more semantic objectives, such as generating tests to reproduce a given bug
report. Reproducing bugs is nonetheless important, as our empirical study shows
that the number of tests added in open source repositories due to issues was
about 28% of the corresponding project test suite size. Meanwhile, due to the
difficulties of transforming the expected program semantics in bug reports into
test oracles, existing failure reproduction techniques tend to deal exclusively
with program crashes, a small subset of all bug reports. To automate test
generation from general bug reports, we propose LIBRO, a framework that uses
Large Language Models (LLMs), which have been shown to be capable of performing
code-related tasks. Since LLMs themselves cannot execute the target buggy code,
we focus on post-processing steps that help us discern when LLMs are effective,
and rank the produced tests according to their validity. Our evaluation of
LIBRO shows that, on the widely studied Defects4J benchmark, LIBRO can generate
failure reproducing test cases for 33% of all studied cases (251 out of 750),
while suggesting a bug reproducing test in first place for 149 bugs. To
mitigate data contamination, we also evaluate LIBRO against 31 bug reports
submitted after the collection of the LLM training data terminated: LIBRO
produces bug reproducing tests for 32% of the studied bug reports. Overall, our
results show LIBRO has the potential to significantly enhance developer
efficiency by automatically generating tests from bug reports.Comment: Accepted to IEEE/ACM International Conference on Software Engineering
2023 (ICSE 2023
Search Based Repair of Deep Neural Networks
Deep Neural Networks (DNNs) are being adopted in various domains, including
safety critical ones. The wide-spread adoption also calls for ways to guide the
testing of their accuracy and robustness, for which various test adequacy
criteria and input generation methods have been recently introduced. In this
paper, we explore the natural subsequent step: given an input that reveals
unexpected behaviour in a trained DNN, we propose to repair the DNN using
input-output pairs as a specification. This paper introduces Arachne, a novel
program repair technique for DNNs. Arachne first performs sensitivity based
fault localisation to limit the number of neural weights it has to modify.
Subsequently, Arachne uses Particle Swarm Optimisation (PSO) to directly
optimise the localised neural weights until the behaviour is corrected. An
empirical study using three different benchmark datasets shows that Arachne can
reduce the instances of the most frequent misclassification type committed by a
pre-trained CIFAR-10 classifier by 27.5%, without any need for additional
training data. Patches generated by Arachne tend to be more focused on the
targeted misbehaviour than DNN retraining, which is more disruptive to
non-targeted behaviour. The overall results suggest the feasibility of patching
DNNs using Arachne until they can be retrained properly
Towards Autonomous Testing Agents via Conversational Large Language Models
Software testing is an important part of the development cycle, yet it
requires specialized expertise and substantial developer effort to adequately
test software. The recent discoveries of the capabilities of large language
models (LLMs) suggest that they can be used as automated testing assistants,
and thus provide helpful information and even drive the testing process. To
highlight the potential of this technology, we present a taxonomy of LLM-based
testing agents based on their level of autonomy, and describe how a greater
level of autonomy can benefit developers in practice. An example use of LLMs as
a testing assistant is provided to demonstrate how a conversational framework
for testing can help developers. This also highlights how the often criticized
hallucination of LLMs can be beneficial while testing. We identify other
tangible benefits that LLM-driven testing agents can bestow, and also discuss
some potential limitations
Influence Of Choice Context On Consumer Decision Making In Global Electronic Commerce
The objective of this paper is to understand how choice context will influence the decision making process of consumers when shopping at electronic shopping malls. This paper provides a framework for exploring context effects on consumer judgment and choice processes in the special case of electronic commerce. First an outline of the judgment process is presented which is used to identify the stages where the context effects may occur. Theliterature in experimental and social psychology, behavioral decision theory and consumer research are selectively reviewed for evidence regarding context effects on judgment. The approach adopted in this paper borrows directly from at least two converging sources : the cross-functional research in judgment and decision making in consumer behavior and cognitive psychology and the research in marketing issues in electronic commerce. The managerial implications of this research are answers to questions such as how best can the firm exploit this new form of transacting business to maximize its leverage in the market place and increase its market share. The academic contribution of this research on context effects is that it helps to reconcile two diverging research streams on judgment and choice (the economic perspective and the behavioral perspective)
The GitHub Recent Bugs Dataset for Evaluating LLM-based Debugging Applications
Large Language Models (LLMs) have demonstrated strong natural language
processing and code synthesis capabilities, which has led to their rapid
adoption in software engineering applications. However, details about LLM
training data are often not made public, which has caused concern as to whether
existing bug benchmarks are included. In lieu of the training data for the
popular GPT models, we examine the training data of the open-source LLM
StarCoder, and find it likely that data from the widely used Defects4J
benchmark was included, raising the possibility of its inclusion in GPT
training data as well. This makes it difficult to tell how well LLM-based
results on Defects4J would generalize, as for any results it would be unclear
whether a technique's performance is due to LLM generalization or memorization.
To remedy this issue and facilitate continued research on LLM-based SE, we
present the GitHub Recent Bugs (GHRB) dataset, which includes 76 real-world
Java bugs that were gathered after the OpenAI data cut-off point
- …