EDBT 2026 Demo / reviewers in the wild / expert

Aryaman Jain

dblp:269/8017 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 2 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
2 papers
Software testing · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software testing
regression testing		 0.512021
TERA: optimizing stochastic regression tests in machine learning projects · ISSTA 2021
Software testing
test optimization		 0.512021
TERA: optimizing stochastic regression tests in machine learning projects · ISSTA 2021
Software testing › flaky test
flaky test detection		 0.412020
Detecting flaky tests in probabilistic and machine learning applications · ISSTA 2020
Software testing › flaky test
test reliability		 0.112020
Detecting flaky tests in probabilistic and machine learning applications · ISSTA 2020

Methods — techniques the papers use, named apart from their topics

hyperparameter tuning · 0.5probabilistic programming · 0.4
YearPublicationVenuePosition
2021 TERA: optimizing stochastic regression tests in machine learning projects
abstract
The stochastic nature of many Machine Learning (ML) algorithms makes testing of ML tools and libraries challenging. ML algorithms allow a developer to control their accuracy and run-time through a set of hyper-parameters, which are typically manually selected in tests. This choice is often too conservative and leads to slow test executions, thereby increasing the cost of regression testing.
Saikat Dutta 0001, Jeeva Selvam, Aryaman Jain, Sasa Misailovic
 ISSTA3
2020 Detecting flaky tests in probabilistic and machine learning applications
abstract
Probabilistic programming systems and machine learning frameworks like Pyro, PyMC3, TensorFlow, and PyTorch provide scalable and efficient primitives for inference and training. However, such operations are non-deterministic. Hence, it is challenging for developers to write tests for applications that depend on such frameworks, often resulting in flaky tests – tests which fail non-deterministically when run on the same version of code.
Saikat Dutta 0001, August Shi, Rutvik Choudhary, Zhekun Zhang, Aryaman Jain, Sasa Misailovic
 ISSTA5