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Michael Petter

dblp:92/2215 · DBLP profile ↗
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9ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0009-7868-651XORCID · corroborated

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

Software engineering, systems software and programming languages · 7 · 1 since 2021Theory of computation · 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
Program analysis · 77% Compilers and program optimization · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Program analysis › static analysis
interprocedural analysis
0.112011
Fast interprocedural linear two-variable equalities · ACM Trans. Program. Lang. Syst. 2011
Compilers and program optimization › register allocation
register coalescing
0.012011
Fast interprocedural linear two-variable equalities · ACM Trans. Program. Lang. Syst. 2011

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

linear algebra · 0.1complexity analysis · 0.1
YearPublicationVenuePosition
2026 Goblint: A Portfolio for Mixed Flow-Sensitive Abstract Interpretation - (Competition Contribution)
Simmo Saan, Ali Rasim Kocal, Michael Petter, Karoliine Holter, Julian Erhard, Michael Schwarz 0007, Vesal Vojdani, Helmut Seidl
TACAS (2)3
2017 Inter-procedural Two-Variable Herbrand Equalities
Stefan Schulze Frielinghaus, Michael Petter, Helmut Seidl
Log. Methods Comput. Sci.2
2015 Inter-procedural Two-Variable Herbrand Equalities
Stefan Schulze Frielinghaus, Michael Petter, Helmut Seidl
ESOP2
2011 Side-Effect Analysis of Assembly Code
Andrea Flexeder, Michael Petter, Helmut Seidl
SAS2
2011 Fast interprocedural linear two-variable equalities
abstract
In this article we provide an interprocedural analysis of linear two-variable equalities. The novel algorithm has a worst-case complexity of 𝒪( n ⋅ k 4 ), where k is the number of variables and n is the program size. Thus, it saves a factor of k 4 in comparison to a related algorithm based on full linear algebra. We also indicate how the practical runtime can be further reduced significantly. The analysis can be applied, for example, for register coalescing, for identifying local variables and thus for interprocedurally observing stack pointer modifications as well as for an analysis of array index expressions, when analyzing low-level code.
Andrea Flexeder, Markus Müller-Olm, Michael Petter, Helmut Seidl
ACM Trans. Program. Lang. Syst.3
2010 Interprocedural Control Flow Reconstruction
Andrea Flexeder, Bogdan Mihaila, Michael Petter, Helmut Seidl
APLAS3
2008 Analysing All Polynomial Equations in
Helmut Seidl, Andrea Flexeder, Michael Petter
SAS3
2007 Interprocedurally Analysing Linear Inequality Relations
Helmut Seidl, Andrea Flexeder, Michael Petter
ESOP3
2006 Interprocedurally Analyzing Polynomial Identities
Markus Müller-Olm, Michael Petter, Helmut Seidl
STACS2