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- research-articleDecember 2021
On the experiences of adopting automated data validation in an industrial machine learning project
ICSE-SEIP '21: Proceedings of the 43rd International Conference on Software Engineering: Software Engineering in PracticePages 248–257https://doi.org/10.1109/ICSE-SEIP52600.2021.00034Background: Data errors are a common challenge in machine learning (ML) projects and generally cause significant performance degradation in ML-enabled software systems. To ensure early detection of erroneous data and avoid training ML models using bad ...
- research-articleDecember 2021
Neural knowledge extraction from cloud service incidents
ICSE-SEIP '21: Proceedings of the 43rd International Conference on Software Engineering: Software Engineering in PracticePages 218–227https://doi.org/10.1109/ICSE-SEIP52600.2021.00031The move from boxed products to services and the widespread adoption of cloud computing has had a huge impact on the software development life cycle and DevOps processes. Particularly, incident management has become critical for developing and operating ...
- research-articleDecember 2021
NNStreamer: efficient and Agile† development of on-device AI systems
- MyungJoo Ham,
- Jijoong Moon,
- Geunsik Lim,
- Jaeyun Jung,
- Hyoungjoo Ahn,
- Wook Song,
- Sangjung Woo,
- Parichay Kapoor,
- Dongju Chae,
- Gichan Jang,
- Yongjoo Ahn,
- Jihoon Lee
ICSE-SEIP '21: Proceedings of the 43rd International Conference on Software Engineering: Software Engineering in PracticePages 198–207https://doi.org/10.1109/ICSE-SEIP52600.2021.00029We propose NNStreamer, a software system that handles neural networks as filters of stream pipelines, applying the stream processing paradigm to deep neural network applications. A new trend with the wide-spread of deep neural network applications is on-...
- research-articleDecember 2021
Learning autocompletion from real-world datasets
ICSE-SEIP '21: Proceedings of the 43rd International Conference on Software Engineering: Software Engineering in PracticePages 131–139https://doi.org/10.1109/ICSE-SEIP52600.2021.00022Code completion is a popular software development tool integrated into all major IDEs. Many neural language models have achieved promising results in completion suggestion prediction on synthetic benchmarks. However, a recent study When Code Completion ...
- research-articleDecember 2021
D2A: a dataset built for AI-based vulnerability detection methods using differential analysis
- Yunhui Zheng,
- Saurabh Pujar,
- Burn Lewis,
- Luca Buratti,
- Edward Epstein,
- Bo Yang,
- Jim Laredo,
- Alessandro Morari,
- Zhong Su
ICSE-SEIP '21: Proceedings of the 43rd International Conference on Software Engineering: Software Engineering in PracticePages 111–120https://doi.org/10.1109/ICSE-SEIP52600.2021.00020Static analysis tools are widely used for vulnerability detection as they understand programs with complex behavior and millions of lines of code. Despite their popularity, static analysis tools are known to generate an excess of false positives. The ...
- research-articleDecember 2021
Robustness of on-device models: adversarial attack to deep learning models on Android apps
ICSE-SEIP '21: Proceedings of the 43rd International Conference on Software Engineering: Software Engineering in PracticePages 101–110https://doi.org/10.1109/ICSE-SEIP52600.2021.00019Deep learning has shown its power in many applications, including object detection in images, natural-language understanding, and speech recognition. To make it more accessible to end users, many deep learning models are now embedded in mobile apps. ...
- research-articleDecember 2021
Asset management in machine learning: a survey
ICSE-SEIP '21: Proceedings of the 43rd International Conference on Software Engineering: Software Engineering in PracticePages 51–60https://doi.org/10.1109/ICSE-SEIP52600.2021.00014Machine Learning (ML) techniques are becoming essential components of many software systems today, causing an increasing need to adapt traditional software engineering practices and tools to the development of ML-based software systems. This need is ...
- research-articleDecember 2021
Using machine intelligence to prioritise code review requests
ICSE-SEIP '21: Proceedings of the 43rd International Conference on Software Engineering: Software Engineering in PracticePages 11–20https://doi.org/10.1109/ICSE-SEIP52600.2021.00010Modern Code Review (MCR) is the process of reviewing new code changes that need to be merged with an existing codebase. As a developer, one may receive many code review requests every day, i.e., the review requests need to be prioritised. Manually ...