Egor Klimov

Research Groups

Biography

I want to explore how people develop software and help them create great products. In particular, I am interested in empirical software engineering, requirements engineering and data engineering.

I graduated from Peter the Great St. Petersburg Polytechnic University with a M.Sc in Mathematics and Computer Science in 2021, same year I graduated from the Computer Science Center with a degree in Software Engineering. In addition to research interests, I have been developing Data Engineering and Business Intelligence systems at Tinkoff Bank since 2018.
 

Publications

Multimodal Recommendation of Messenger Channels

May 2022

Ekaterina Koshchenko, Egor Klimov, Vladimir Kovalenko

Collaboration platforms, such as GitHub and Slack, are a vital instrument in the day-to-day routine of software engineering teams. The data stored in these platforms has a significant value for datadriven methods that assist with decision-making and help improve software quality. However, the distribution of this data across different platforms leads to the fact that combining it is a very timeconsuming process. Most existing algorithms for socio-technical assistance, such as recommendation systems, are based only on data directly related to the purpose of the algorithms, often originating from a single system. In this work, we explore the capabilities of a multimodal recommendation system in the context of software engineering. Using records of interaction between employees in a software company in messenger channels and repositories, as well as the organizational structure, we build several channel recommendation models for a software engineering collaboration platform, and compare them on historical data. In addition, we implement a channel recommendation bot and assess the quality of recommendations from the best models with a user study. We find that the multimodal recommender yields better recommendations than unimodal baselines, allows to mitigate the overfitting problem, and helps to deal with cold start. Our findings suggest that the multimodal approach is promising for other recommendation problems in software engineering.

MSR 2022

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