Large language models for software engineering: A systematic literature review
Large Language Models (LLMs) have significantly impacted numerous domains, including
Software Engineering (SE). Many recent publications have explored LLMs applied to …
Software Engineering (SE). Many recent publications have explored LLMs applied to …
A survey on deep learning for software engineering
In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)”
and an improved model training method to break the bottleneck of neural network …
and an improved model training method to break the bottleneck of neural network …
Learning selective self-mutual attention for RGB-D saliency detection
Saliency detection on RGB-D images is receiving more and more research interests
recently. Previous models adopt the early fusion or the result fusion scheme to fuse the input …
recently. Previous models adopt the early fusion or the result fusion scheme to fuse the input …
Spt-code: Sequence-to-sequence pre-training for learning source code representations
Recent years have seen the successful application of large pre-trained models to code
representation learning, resulting in substantial improvements on many code-related …
representation learning, resulting in substantial improvements on many code-related …
What do they capture? a structural analysis of pre-trained language models for source code
Recently, many pre-trained language models for source code have been proposed to model
the context of code and serve as a basis for downstream code intelligence tasks such as …
the context of code and serve as a basis for downstream code intelligence tasks such as …
A survey on machine learning techniques for source code analysis
The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …
these techniques to a myriad of software engineering tasks that use source code analysis …
A comprehensive investigation of multimodal deep learning fusion strategies for breast cancer classification
In breast cancer research, diverse data types and formats, such as radiological images,
clinical records, histological data, and expression analysis, are employed. Given the intricate …
clinical records, histological data, and expression analysis, are employed. Given the intricate …
Improving code search with co-attentive representation learning
Searching and reusing existing code from a large-scale codebase, eg, GitHub, can help
developers complete a programming task efficiently. Recently, Gu et al. proposed a deep …
developers complete a programming task efficiently. Recently, Gu et al. proposed a deep …
Learning selective mutual attention and contrast for RGB-D saliency detection
How to effectively fuse cross-modal information is a key problem for RGB-D salient object
detection. Early fusion and result fusion schemes fuse RGB and depth information at the …
detection. Early fusion and result fusion schemes fuse RGB and depth information at the …
You see what i want you to see: poisoning vulnerabilities in neural code search
Searching and reusing code snippets from open-source software repositories based on
natural-language queries can greatly improve programming productivity. Recently, deep …
natural-language queries can greatly improve programming productivity. Recently, deep …