filipe r. cogo

Welcome! I'm a Software Engineering Researcher (Principal Engineer) at Huawei Technologies Co., based in the beautiful city of Kingston, Ontario, Canada. My research typically adopts machine learning and mining software repositories to investigate and propose automated solutions to technical and social problems in software engineering. I'm actively researching related topics with dependency management, vulnerability management, verifiable builds, code review, and programming languages documentation.

I received a Ph.D. in Computer Science from the School of Computing at Queen's University, under the supervision of Prof. Ahmed E. Hassan. During my Ph.D. (2017-2020), I was fortunate to work with a team of talented students and researchers at the Software Analysis and Intelligence Lab (SAIL). I also received a master's (2012) and bachelor's (2009) degree in Computer Science from the Department of Informatics (Departamento de Informática) at the State University of Maringa (Universidade Estadual de Maringá). Prior to my current position, I was an associate professor (professor adjunto) in the Department of Computing of the Federal University of Technology at Paraná (Campo Mourão campus) and an assistant professor at UniCesumar.

Latest news

[June 2022] Our paper "Automated Unearthing of Dangerous Issue Reports" was accepted by ESEC/FSE. This paper describes a deep learning-based approach to identifying issue reports that will eventually be linked to a common vulnerability and exposure (CVE) record. Pre-print soon.

[June 2022] Our paper entitled "Assessing the Alignment Between the Information Needs of Developers and the Documentation of Programming Languages: A Case Study on Rust." was accepted by TOSEM. We present a topic modelling-based approach to evaluate how the documentation of Rust aligns with developers' information needs, as represented by their discussions in Q&A forums. Pre-print can be found here.

[April 2022] Our paper entitled "Understanding the Customization of Dependency Bots: The Case of Dependabot" was accepted by IEEE Software. This paper provides bot designers and software developers with insights into the compromises that should be carefully considered regarding the customization features of dependency bots. Pre-print can be found here.