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Haaken Martinson Baldwin

dblp:246/5349 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

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

Software engineering, systems software and programming languages · 1

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
Program analysis · 100%
Network and information security
1 paper
Systems and software security · 100%

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

TopicWeightPapersLastEvidence papers
Program analysis › specification mining
error specification inference
0.412019
Effective error-specification inference via domain-knowledge expansion · ESEC/SIGSOFT FSE 2019
Program analysis
static analysis
0.412019
Effective error-specification inference via domain-knowledge expansion · ESEC/SIGSOFT FSE 2019
Systems and software security › software vulnerability
error handling bugs
0.112019
Effective error-specification inference via domain-knowledge expansion · ESEC/SIGSOFT FSE 2019

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

intraprocedural flow-sensitive analysis · 0.8interprocedural context-insensitive analysis · 0.8
YearPublicationVenuePosition
2019 Effective error-specification inference via domain-knowledge expansion
abstract
Error-handling code responds to the occurrence of runtime errors. Failure to correctly handle errors can lead to security vulnerabilities and data loss. This paper deals with error handling in software written in C that uses the return-code idiom: the presence and type of error is encoded in the return value of a function. This paper describes EESI, a static analysis that infers the set of values that a function can return on error. Such a function error-specification can then be used to identify bugs related to incorrect error handling. The key insight of EESI is to bootstrap the analysis with domain knowledge related to error handling provided by a developer. EESI uses a combination of intraprocedural, flow-sensitive analysis and interprocedural, context-insensitive analysis to ensure precision and scalability. We built a tool ECC to demonstrate how the function error-specifications inferred by EESI can be used to automatically find bugs related to incorrect error handling. ECC detected 246 bugs across 9 programs, of which 110 have been confirmed. ECC detected 220 previously unknown bugs, of which 99 are confirmed. Two patches have already been merged into OpenSSL.
Daniel DeFreez, Haaken Martinson Baldwin, Cindy Rubio-González, Aditya V. Thakur
ESEC/SIGSOFT FSE2