For Media

Read my Research

Globalize: Silicon Valley Conference - Unveiling Insights on Software Engineering Productivity

Date

Oct 2023

At the Globalize Silicon Valley Conference, held at the Google Cloud Campus, Yegor Denisov-Blanch, a researcher from Stanford University, presented his latest findings on software engineering productivity.

His research introduces an algorithmic model designed to automate aspects of code review, such as assessing coding time, implementation time, and code complexity.

Denisov-Blanch's work is based on extensive data analysis involving over 50,000 engineers from hundreds of companies. His research has garnered attention in the tech community, with discussions about its implications appearing in various media outlets.

The conference provided a platform for Denisov-Blanch to share his insights with a diverse audience, including investors, entrepreneurs, and fellow researchers, highlighting the importance of data-driven approaches in enhancing software engineering productivity.

More Research & Conferences

Roundtable on Engineering Excellence

Engineering excellence remained a critical focus for Financial Services Institutions aiming to stay competitive and innovative in an ever-evolving landscape. With the rise of GenAI, there was growing interest in understanding how it could be leveraged to enhance the software development lifecycle and improve engineering productivity.

Roundtable on Engineering Excellence

Engineering excellence remained a critical focus for Financial Services Institutions aiming to stay competitive and innovative in an ever-evolving landscape. With the rise of GenAI, there was growing interest in understanding how it could be leveraged to enhance the software development lifecycle and improve engineering productivity.

Roundtable on Engineering Excellence

Engineering excellence remained a critical focus for Financial Services Institutions aiming to stay competitive and innovative in an ever-evolving landscape. With the rise of GenAI, there was growing interest in understanding how it could be leveraged to enhance the software development lifecycle and improve engineering productivity.

DPE Summit 2024: Measuring Software Engineering Productivity

Approaches to Measuring Software Engineering Productivity

DPE Summit 2024: Measuring Software Engineering Productivity

Approaches to Measuring Software Engineering Productivity

DPE Summit 2024: Measuring Software Engineering Productivity

Approaches to Measuring Software Engineering Productivity

Research on predicting expert valuations in code review

Manual code reviews are an essential but time-consuming part of software development, often leading reviewers to prioritize technical issues while skipping valuable assessments. This paper presents an algorithmic model that automates aspects of code review typically avoided due to their complexity or subjectivity, such as assessing coding time, implementation time, and code complexity. Instead of replacing manual reviews, our model adds insights that help reviewers focus on more impactful tasks. Calibrated using expert evaluations, the model predicts key metrics from code commits with strong correlations to human judgments (r = 0.82 for coding time, r = 0.86 for implementation time). By automating these assessments, we reduce the burden on human reviewers and ensure consistent analysis of time-consuming areas, offering a scalable solution alongside manual reviews. This research shows how automated tools can enhance code reviews by addressing overlooked tasks, supporting data-driven decisions and improving the review process.

Research on predicting expert valuations in code review

Manual code reviews are an essential but time-consuming part of software development, often leading reviewers to prioritize technical issues while skipping valuable assessments. This paper presents an algorithmic model that automates aspects of code review typically avoided due to their complexity or subjectivity, such as assessing coding time, implementation time, and code complexity. Instead of replacing manual reviews, our model adds insights that help reviewers focus on more impactful tasks. Calibrated using expert evaluations, the model predicts key metrics from code commits with strong correlations to human judgments (r = 0.82 for coding time, r = 0.86 for implementation time). By automating these assessments, we reduce the burden on human reviewers and ensure consistent analysis of time-consuming areas, offering a scalable solution alongside manual reviews. This research shows how automated tools can enhance code reviews by addressing overlooked tasks, supporting data-driven decisions and improving the review process.

Research on predicting expert valuations in code review

Manual code reviews are an essential but time-consuming part of software development, often leading reviewers to prioritize technical issues while skipping valuable assessments. This paper presents an algorithmic model that automates aspects of code review typically avoided due to their complexity or subjectivity, such as assessing coding time, implementation time, and code complexity. Instead of replacing manual reviews, our model adds insights that help reviewers focus on more impactful tasks. Calibrated using expert evaluations, the model predicts key metrics from code commits with strong correlations to human judgments (r = 0.82 for coding time, r = 0.86 for implementation time). By automating these assessments, we reduce the burden on human reviewers and ensure consistent analysis of time-consuming areas, offering a scalable solution alongside manual reviews. This research shows how automated tools can enhance code reviews by addressing overlooked tasks, supporting data-driven decisions and improving the review process.