To promote communications of new or on-going research in areas at the intersection of SE and ML/AI, SEMLA accepts two types of research contributions from the community: 1) poster presentation and 2) research talk. The accepted posters will be presented in a dedicated session; the accepted research talks will be presented together with other invited talks.
A research contribution can be about a recently completed or on-going project that has been published or is publishable. The research contribution consists of two components:
- Submitting a research abstract (maximum 300 words)
- Participating in a poster presentation – or delivering a research talk at the SEMLA symposium.
SEMLA 2023 is offering a travel grant sponsored by Airudi Inc. to assist students with travel costs associated with attending the event. For more information, please check our travel support page.
Submission of a research abstract
To participate, submit a research abstract related to the main themes of SEMLA.
The abstract should include the following elements: the aim, approach and novelty, results, and conclusion. Your submission must present the research contributions of the authors and it could have been published elsewhere.
The abstracts will be evaluated based on their relevance to SEMLA 2023 theme and their quality. The review process is single-blinded. Please consult the following form (submission link) to submit your poster or research talk. You will be asked to include the following information:
- Title
- Author name(s) (with the presenter(s) highlighted) and affiliation
- Abstract
- Type (poster or research talk)
Participating in a poster presentation or research talk
The committee members will review the submissions and will invite you to present your work either as a poster or a research talk during a conference. Please note that due to the limited slots for research talks, a submission for research talk may be invited to be presented as a poster.
Deadlines
Submission deadline: May 15, 2023
Acceptance notifications: May 18, 2023
Please contact Prof. Zorhreh Sharafi (zohreh.sharafi@polymtl.ca) for any questions.
Abstract: Anomaly detection plays an important role in management of modern large-scale distributed systems. Logs, which record system runtime information, are widely used for anomaly detection. However, Unsupervised anomaly detection algorithms face challenges in addressing complex systems, which generate vast amounts of multivariate time series data. Timely anomaly detection is crucial for managing these systems effectively and minimizing downtime. This proactive approach minimizes system downtime and plays a vital role in incident management for large-scale systems. To address these challenges, a method called Multi-Scale Convolutional Recurrent Encoder-Decoder (MSCRED) has been developed for detecting anomalies in CN PTC system logs. MSCRED leverages the power of multivariate time series data to perform anomaly detection and diagnosis. It creates multi-scale signature matrices that capture different levels of system statuses across various time steps. The method utilizes a convolutional encoder to capture inter-sensor correlations and a Convolutional Long-Short Term Memory (ConvLSTM) network with attention mechanisms to capture temporal patterns.
Abstract
Abstract: Language models such as RoBERTa, CodeBERT, and GraphCodeBERT have gotten much attention in the past three years for various Software Engineering tasks. Though these models are proven to have state-of-the-art performance for many SE tasks, such as code summarization, they often require to be fully fine-tuned for the downstream task. Is there a better way for fine-tuning these models that require training fewer parameters? Can we impose new information on the current models without pre-training them again? How do these models perform for different programming languages, especially low-resource ones with less training data available? How can we use the knowledge learned from other programming languages to improve the performance of low-resource languages? This talk will review a series of experiments and our contributions to answering these questions.
