Celal Ziftci
Celal Ziftci is a Software Engineer at Google.
He received his BS from Bilkent University in Turkey, working on Natural Language Processing.
He received his MS from University of Illinois Urbana-Champaign (UIUC), working on Computer Vision and Machine Learning.
Finally, he received his PhD from University of California San Diego (UCSD), working on software testing and maintenance.
His research interests are software testing, software analytics, program analysis, and applications of data mining and machine learning in these fields.
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Authored Publications
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Improving Design Reviews at Google
Ben Greenberg
International Conference on Automated Software Engineering, IEEE/ACM (2023)
Preview abstract
Design review is an important initial phase of the software development life-cycle where stakeholders gain and discuss early insights into the design’s viability, discover potentially costly mistakes, and identify inconsistencies and inadequacies. For improved development velocity, it is important that design owners get their designs approved as quickly as possible.
In this paper, we discuss how engineering design reviews are typically conducted at Google, and propose a novel, structured, automated solution to improve design review velocity. Based on data collected on 141,652 approved documents authored by 41,030 users over four years, we show that our proposed solution decreases median time-to-approval by 25%, and provides further gains when used consistently. We also provide qualitative data to demonstrate our solution’s success, discuss factors that impact design review latency, propose strategies to tackle them, and share lessons learned from the usage of our solution.
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De-Flake Your Tests: Automatically Locating Root Causes of Flaky Tests in Code At Google
Diego Cavalcanti
International Conference on Software Maintenance and Evolution (ICSME) 2020, IEEE
Preview abstract
Regression testing is a critical part of software development and maintenance.
It ensures that modifications to existing software do not break existing
behavior and functionality.
One of the key assumptions about regression tests is that their results are
deterministic: when executed without any modifications with the same
configuration, either they always fail or they always pass. In practice,
however, there exist tests that are non-deterministic, called flaky
tests. Flaky tests cause the results of test runs to be unreliable, and they
disrupt the software development workflow.
In this paper, we present a novel technique to automatically identify the
locations of the root causes of flaky tests on the code level to help
developers debug and fix them. We study the technique on flaky tests across
428 projects at Google.
Based on our case studies, the technique helps identify the location of the root
causes of flakiness with 82% accuracy. Furthermore, our studies show that
integration into the appropriate developer workflows, simplicity of debugging
aides and fully automated fixes are crucial and preferred components for
adoption and usability of flakiness debugging and fixing tools.
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Who Broke the Build? Automatically Identifying Changes That Induce Test Failures In Continuous Integration at Google Scale
Proceedings of the 39th International Conference on Software Engineering: Software Engineering in Practice Track, IEEE Press, Buenos Aires, Argentina (2017), pp. 113-122 (to appear)
Preview abstract
Quickly identifying and fixing code changes that
introduce regressions is critical to keep the momentum on
software development, especially in very large scale software
repositories with rapid development cycles, such as at Google.
Identifying and fixing such regressions is one of the most
expensive, tedious, and time consuming tasks in the software
development life-cycle. Therefore, there is a high demand
for automated techniques that can help developers identify
such changes while minimizing manual human intervention.
Various techniques have recently been proposed to identify such
code changes. However, these techniques have shortcomings
that make them unsuitable for rapid development cycles as
at Google. In this paper, we propose a novel algorithm to
identify code changes that introduce regressions, and discuss
case studies performed at Google on 140 projects. Based on our
case studies, our algorithm automatically identifies the change
that introduced the regression in the top-5 among thousands of
candidates 82% of the time, and provides considerable savings
on manual work developers need to perform
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