scholarly journals Search4Code: Code Search Intent Classification Using Weak Supervision

Author(s):  
Nikitha Rao ◽  
Chetan Bansal ◽  
Joe Guan
2015 ◽  
Author(s):  
Judith Gaspers ◽  
Philipp Cimiano ◽  
Britta Wrede

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sabri Eyuboglu ◽  
Geoffrey Angus ◽  
Bhavik N. Patel ◽  
Anuj Pareek ◽  
Guido Davidzon ◽  
...  

AbstractComputational decision support systems could provide clinical value in whole-body FDG-PET/CT workflows. However, limited availability of labeled data combined with the large size of PET/CT imaging exams make it challenging to apply existing supervised machine learning systems. Leveraging recent advancements in natural language processing, we describe a weak supervision framework that extracts imperfect, yet highly granular, regional abnormality labels from free-text radiology reports. Our framework automatically labels each region in a custom ontology of anatomical regions, providing a structured profile of the pathologies in each imaging exam. Using these generated labels, we then train an attention-based, multi-task CNN architecture to detect and estimate the location of abnormalities in whole-body scans. We demonstrate empirically that our multi-task representation is critical for strong performance on rare abnormalities with limited training data. The representation also contributes to more accurate mortality prediction from imaging data, suggesting the potential utility of our framework beyond abnormality detection and location estimation.


2021 ◽  
Vol 28 (2) ◽  
Author(s):  
Sebastian Nielebock ◽  
Robert Heumüller ◽  
Kevin Michael Schott ◽  
Frank Ortmeier

AbstractLack of experience, inadequate documentation, and sub-optimal API design frequently cause developers to make mistakes when re-using third-party implementations. Such API misuses can result in unintended behavior, performance losses, or software crashes. Therefore, current research aims to automatically detect such misuses by comparing the way a developer used an API to previously inferred patterns of the correct API usage. While research has made significant progress, these techniques have not yet been adopted in practice. In part, this is due to the lack of a process capable of seamlessly integrating with software development processes. Particularly, existing approaches do not consider how to collect relevant source code samples from which to infer patterns. In fact, an inadequate collection can cause API usage pattern miners to infer irrelevant patterns which leads to false alarms instead of finding true API misuses. In this paper, we target this problem (a) by providing a method that increases the likelihood of finding relevant and true-positive patterns concerning a given set of code changes and agnostic to a concrete static, intra-procedural mining technique and (b) by introducing a concept for just-in-time API misuse detection which analyzes changes at the time of commit. Particularly, we introduce different, lightweight code search and filtering strategies and evaluate them on two real-world API misuse datasets to determine their usefulness in finding relevant intra-procedural API usage patterns. Our main results are (1) commit-based search with subsequent filtering effectively decreases the amount of code to be analyzed, (2) in particular method-level filtering is superior to file-level filtering, (3) project-internal and project-external code search find solutions for different types of misuses and thus are complementary, (4) incorporating prior knowledge of the misused API into the search has a negligible effect.


Author(s):  
P. Niranjan ◽  
Syed Abdul Moeed ◽  
V. Pranitha ◽  
T. Sam Spurgeon ◽  
V. Kavitha ◽  
...  
Keyword(s):  

2014 ◽  
Vol 49 (6) ◽  
pp. 349-360 ◽  
Author(s):  
Yaniv David ◽  
Eran Yahav
Keyword(s):  

Author(s):  
Jianhang Shuai ◽  
Ling Xu ◽  
Chao Liu ◽  
Meng Yan ◽  
Xin Xia ◽  
...  

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