Component-based product line engineering with UML
Component-based product line engineering with UML
Generation Scavenging: A non-disruptive high performance storage reclamation algorithm
SDE 1 Proceedings of the first ACM SIGSOFT/SIGPLAN software engineering symposium on Practical software development environments
Feature-Oriented Programming and the AHEAD Tool Suite
Proceedings of the 26th International Conference on Software Engineering
Automated Detection of Performance Regressions: The Mono Experience
MASCOTS '05 Proceedings of the 13th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
IEEE Transactions on Software Engineering
FeatureMapper: mapping features to models
Companion of the 30th international conference on Software engineering
FEATUREHOUSE: Language-independent, automated software composition
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Selecting highly optimal architectural feature sets with Filtered Cartesian Flattening
Journal of Systems and Software
Efficient runtime tracking of allocation sites in Java
Proceedings of the 6th ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
Automated analysis of feature models 20 years later: A literature review
Information Systems
The DOPLER meta-tool for decision-oriented variability modeling: a multiple case study
Automated Software Engineering
Approaching Non-functional Properties of Software Product Lines: Learning from Products
APSEC '10 Proceedings of the 2010 Asia Pacific Software Engineering Conference
Mapping features to models: a template approach based on superimposed variants
GPCE'05 Proceedings of the 4th international conference on Generative Programming and Component Engineering
ICPE '12 Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering
Automated detection of performance regressions using statistical process control techniques
ICPE '12 Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering
Automatically finding performance problems with feedback-directed learning software testing
Proceedings of the 34th International Conference on Software Engineering
Information and Software Technology
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Non-functional properties such as memory footprint have recently gained importance in software product line research. However, determining the memory characteristics of individual features and product variants is extremely challenging. We present an approach that supports the monitoring of memory characteristics of individual features at the level of Java virtual machines. Our approach provides extensions to Java virtual machines to track memory allocations and deal-locations of individual features based on a feature-to-code mapping. The approach enables continuous monitoring at the level of features to detect anomalies such as memory leaks, excessive memory consumption, or abnormal garbage collection times in product variants. We provide an evaluation of our approach based on different product variants of the DesktopSearcher product line. Our experiment with different program inputs demonstrates the feasibility of our technique.