Philipp Leitner
Philipp Leitner is an Associate Professor at the Division of Interaction Design and Software Engineering. He currently leads the unit Software Engineering 2. Before moving to Sweden in 2017, he was a senior researcher at the University of Zurich and a PhD student / postdoc at TU Vienna. Philipp's research is primarily on the development of modern, web- and cloud-based systems. Particularly, he is interested in performance measurement and optimization, architectures for scalable and resilient systems, and release engineering processes. He mostly uses empirical research methods, such as benchmarking, repository mining, interviews, or observational methods.
Visar 53 publikationer
A unified active learning framework for annotating graph data for regression task
Machine Learning Systems are Bloated and Vulnerable
The roles, responsibilities, and skills of engineers in the era of microservices-based architectures
Beyond Code Generation: An Observational Study of ChatGPT Usage in Software Engineering Practice
Machine learning systems are bloated and vulnerable
From Human-to-Human to Human-to-Bot Conversations in Software Engineering
Machine learning systems are bloated and vulnerable
The Impact of Compiler Warnings on Code Quality in C++ Projects
Automated Generation and Evaluation of JMH Microbenchmark Suites From Unit Tests
An empirical study of the systemic and technical migration towards microservices
The Perceived Impact and Sequence of Activities When Transitioning to Microservices
Towards Continuous Performance Assessment of Java Applications With PerfBot
Batch Mode Deep Active Learning for Regression on Graph Data
Using Microbenchmark Suites to Detect Application Performance Changes
TriggerBench: A Performance Benchmark for Serverless Function Triggers
Using Benchmarking Bots for Continuous Performance Assessment
CrossFit: Fine-grained Benchmarking of Serverless Application Performance across Cloud Providers
An Empirical Analysis of Microservices Systems Using Consumer-Driven Contract Testing
TEP-GNN: Accurate Execution Time Prediction of Functional Tests Using Graph Neural Networks
A systematic mapping study of source code representation for deep learning in software engineering
Dependency management bots in open-source systems—prevalence and adoption
What's Wrong With My Benchmark Results? Studying Bad Practices in JMH Benchmarks
Applying test case prioritization to software microbenchmarks
The Migration Journey Towards Microservices
Facing the giant: A grounded theory study of decision-making in microservices migrations
Emerging Trends, Challenges, and Experiences in DevOps and Microservice APIs
Function-as-a-Service Performance Evaluation: A Multivocal Literature Review
Topology-aware continuous experimentation in microservice-based applications
A mixed-method empirical study of Function-as-a-Service software development in industrial practice
Interactive Production Performance Feedback in the IDE
Cachematic – Automatic Invalidation in Application-Level Caching Systems
Performance Benchmarking of Infrastructure-as-a-Service (IaaS) Clouds with CloudWorkBench
Current and Future Bots in Software Development
Transpiling Applications into Optimized Serverless Orchestrations
Software Microbenchmarking in the Cloud. How Bad is it Really?
Continuous Experimentation - Challenges, Implementation Techniques, and Current Research
Estimating Cloud Application Performance Based on Micro-Benchmark Profiling
Search-Based Scheduling of Experiments in Continuous Deployment
PerformanceHat - Augmenting Source Code with Runtime Performance Traces in the IDE
We’re Doing It Live: A Multi-Method Empirical Study on Continuous Experimentation
A Cloud Benchmark Suite Combining Micro and Applications Benchmarks
An Evaluation of Open-Source Software Microbenchmark Suites for Continuous Performance Assessment
Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World
Optimized IoT service placement in the fog
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Lastbilsarkitektur för funktionalitet i molnet (TrAF-Cloud)
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