123 citations · 443 across the 21 of their papers we have counts for
11 papers · 1 filter
Learning to Learn to Predict Performance Regressions in Production at Meta
Moritz Beller, Hongyu Li, Vivek Nair +6
Catching and attributing code change-induced performance regressions in production is hard; predicting them beforehand, even harder. A primer on automatically learning to predict p…
Whence to Learn? Transferring Knowledge in Configurable Systems using BEETLE
Rahul Krishna, Vivek Nair, Pooyan Jamshidi +1
As software systems grow in complexity and the space of possible configurations increases exponentially, finding the near-optimal configuration of a software system becomes challen…
Transfer Learning with Bellwethers to find Good Configurations
Vivek Nair, Rahul Krishna, Tim Menzies +1
As software systems grow in complexity, the space of possible configurations grows exponentially. Within this increasing complexity, developers, maintainers, and users cannot keep…
Data-Driven Search-based Software Engineering
Vivek Nair, Amritanshu Agrawal, Jianfeng Chen +6
This paper introduces Data-Driven Search-based Software Engineering (DSE), which combines insights from Mining Software Repositories (MSR) and Search-based Software Engineering (SB…
Finding Faster Configurations using FLASH
Vivek Nair, Zhe Yu, Tim Menzies +2
Finding good configurations for a software system is often challenging since the number of configuration options can be large. Software engineers often make poor choices about conf…
FLASH: A Faster Optimizer for SBSE Tasks
Vivek Nair, Zhe Yu, Tim Menzies
Most problems in search-based software engineering involve balancing conflicting objectives. Prior approaches to this task have required a large number of evaluations- making them…
Using Bad Learners to find Good Configurations
Vivek Nair, Tim Menzies, Norbert Siegmund +1
Finding the optimally performing configuration of a software system for a given setting is often challenging. Recent approaches address this challenge by learning performance model…
Faster Discovery of Faster System Configurations with Spectral Learning
Vivek Nair, Tim Menzies, Norbert Siegmund +1
Despite the huge spread and economical importance of configurable software systems, there is unsatisfactory support in utilizing the full potential of these systems with respect to…
Beyond Evolutionary Algorithms for Search-based Software Engineering
Jianfeng Chen, Vivek Nair, Tim Menzies
Context: Evolutionary algorithms typically require a large number of evaluations (of solutions) to converge - which can be very slow and expensive to evaluate.Objective: To solve s…
Why is Differential Evolution Better than Grid Search for Tuning Defect Predictors?
Wei Fu, Vivek Nair, Tim Menzies
Context: One of the black arts of data mining is learning the magic parameters which control the learners. In software analytics, at least for defect prediction, several methods, l…
"Sampling"' as a Baseline Optimizer for Search-based Software Engineering
Jianfeng Chen, Vivek Nair, Rahul Krishna +1
Increasingly, Software Engineering (SE) researchers use search-based optimization techniques to solve SE problems with multiple conflicting objectives. These techniques often apply…