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Andy Maule

dblp:75/5092 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2008
—ORCID · none

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

Software engineering, systems software and programming languages · 1 · 1 first-author

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%
Databases, data mining, and information retrieval
1 paper
Data models and query languages · 100%

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

TopicWeightPapersLastEvidence papers
Program analysis
data flow analysis
0.112008
Impact analysis of database schema changes · ICSE 2008
Program analysis
static analysis
0.112008
Impact analysis of database schema changes · ICSE 2008
Data models and query languages › schema management
schema evolution
0.012008
Impact analysis of database schema changes · ICSE 2008

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

program slicing · 0.2context-sensitive analysis · 0.2
YearPublicationVenuePosition
2008 Impact analysis of database schema changes
abstract
We propose static program analysis techniques for identifying the impact of relational database schema changes upon object-oriented applications. We use dataflow analysis to extract all possible database interactions that an application may make. We then use this information to predict the effects of schema change. We evaluate our approach with a case-study of a commercially available content management system, where we investigated 62 versions of between 70k-127k LoC and a schema size of up to 101 tables and 568 stored procedures. We demonstrate that the program analysis must be more precise, in terms of context-sensitivity than related work. However, increasing the precision of this analysis increases the computational cost. We use program slicing to reduce the size of the program that needs to be analyzed. Using this approach, we are able to analyse the case study in under 2 minutes on a standard desktop machine, with no false negatives and a low level of false positives.
Andy Maule, Wolfgang Emmerich, David S. Rosenblum
ICSE1