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Raluca Sauciuc

dblp:123/7868 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none

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

Software engineering, systems software and programming languages · 2

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
1 paper
Software maintenance and evolution · 67% Program analysis · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software maintenance and evolution
build systems
0.212015
Automated Decomposition of Build Targets · ICSE (1) 2015
Program analysis › static analysis
dependency analysis
0.212015
Automated Decomposition of Build Targets · ICSE (1) 2015
Software maintenance and evolution
software modularity
0.212015
Automated Decomposition of Build Targets · ICSE (1) 2015

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

strongly connected component analysis · 0.2greedy algorithm · 0.2
YearPublicationVenuePosition
2015 Automated Decomposition of Build Targets
abstract
A (build) target specifies the information that is needed to automatically build a software artifact. This paper focuses on underutilized targets - an important dependency problem that we identified at Google. An underutilized target is one with files not needed by some of its dependents. Underutilized targets result in less modular code, overly large artifacts, slow builds, and unnecessary build and test triggers. To mitigate these problems, programmers decompose underutilized targets into smaller targets. However, manually decomposing a target is tedious and error-prone. Although we prove that finding the best target decomposition is NP-hard, we introduce a greedy algorithm that proposes a decomposition through iterative unification of the strongly connected components of the target. Our tool found that 19,994 of 40,000 Java library targets at Google can be decomposed to at least two targets. The results show that our tool is (1) efficient because it analyzes a target in two minutes on average and (2) effective because for each of 1,010 targets, it would save at least 50% of the total execution time of the tests triggered by the target.
Mohsen Vakilian, Raluca Sauciuc, J. David Morgenthaler, Vahab S. Mirrokni
ICSE (1)2
2012 Building Useful Program Analysis Tools Using an Extensible Java Compiler
abstract
Large software companies need customized tools to manage their source code. These tools are often built in an ad-hoc fashion, using brittle technologies such as regular expressions and home-grown parsers. Changes in the language cause the tools to break. More importantly, these ad-hoc tools often do not support uncommon-but-valid code code patterns. We report our experiences building source-code analysis tools at Google on top of a third-party, open-source, extensible compiler. We describe three tools in use on our Java code base. The first, Strict Java Dependencies, enforces our dependency policy in order to reduce JAR file sizes and testing load. The second, error-prone, adds new error checks to the compilation process and automates repair of those errors at a whole-code base scale. The third, Thindex, reduces the indexing burden for a Java IDE so that it can support Google-sized projects.
Edward Aftandilian, Raluca Sauciuc, Siddharth Priya, Sundaresan Krishnan
SCAM2