Personal Information
Unternehmen/Arbeitsplatz
Greater Seattle Area United States
Beruf
Research Intern at Microsoft | PhD Software Engineering | Data Science, Machine Learning Enthusiast
Branche
Technology / Software / Internet
Webseite
kochharps.wix.com/pavneet
Info
- Currently working as an intern at Microsoft Research on understanding how different Microsoft teams use software analytics to drive development.
- Conducting user-research studies including in-depth interviews, surveys to understand pain-points of software engineers and providing recommendations.
- Analyzing large datasets from platforms such as GitHub, JIRA to help software engineers make data-driven decisions.
- Applying machine learning techniques (supervised and unsupervised) such as classification, regression, topic modeling, natural language processing (text mining) etc. to solve several software engineering problems.
Tags
empirical study
software testing
software
lda
github
software bugs
stackoverflow
regression
code coverage
smartphone
windows
statistics
mobile
software mining
catalog
reppositories
data mining
usability
api
functionality
programmableweb
software developers
asserts
lines of code
fault localization
literature review
software practitioners
programming languages
software quality
adequacy
test cases
microsoft
test automation culture
app developers
android
bugs
test suite effectiveness
software metrics
test adequacy
bug localization
misclassification
fine-grained
issue reports
reclassification
open source
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Präsentationen
(14)Personal Information
Unternehmen/Arbeitsplatz
Greater Seattle Area United States
Beruf
Research Intern at Microsoft | PhD Software Engineering | Data Science, Machine Learning Enthusiast
Branche
Technology / Software / Internet
Webseite
kochharps.wix.com/pavneet
Info
- Currently working as an intern at Microsoft Research on understanding how different Microsoft teams use software analytics to drive development.
- Conducting user-research studies including in-depth interviews, surveys to understand pain-points of software engineers and providing recommendations.
- Analyzing large datasets from platforms such as GitHub, JIRA to help software engineers make data-driven decisions.
- Applying machine learning techniques (supervised and unsupervised) such as classification, regression, topic modeling, natural language processing (text mining) etc. to solve several software engineering problems.
Tags
empirical study
software testing
software
lda
github
software bugs
stackoverflow
regression
code coverage
smartphone
windows
statistics
mobile
software mining
catalog
reppositories
data mining
usability
api
functionality
programmableweb
software developers
asserts
lines of code
fault localization
literature review
software practitioners
programming languages
software quality
adequacy
test cases
microsoft
test automation culture
app developers
android
bugs
test suite effectiveness
software metrics
test adequacy
bug localization
misclassification
fine-grained
issue reports
reclassification
open source
Mehr anzeigen