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.
Showing 53 publications
A unified active learning framework for annotating graph data for regression task
Beyond Code Generation: An Observational Study of ChatGPT Usage in Software Engineering Practice
Machine Learning Systems are Bloated and Vulnerable
The roles, responsibilities, and skills of engineers in the era of microservices-based architectures
From Human-to-Human to Human-to-Bot Conversations in Software Engineering
Machine learning systems are bloated and vulnerable
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
The Perceived Impact and Sequence of Activities When Transitioning to Microservices
Towards Continuous Performance Assessment of Java Applications With PerfBot
An empirical study of the systemic and technical migration towards microservices
Batch Mode Deep Active Learning for Regression on Graph Data
Using Microbenchmark Suites to Detect Application Performance Changes
Using Benchmarking Bots for Continuous Performance Assessment
TriggerBench: A Performance Benchmark for Serverless Function Triggers
CrossFit: Fine-grained Benchmarking of Serverless Application Performance across Cloud Providers
A systematic mapping study of source code representation for deep learning in software engineering
TEP-GNN: Accurate Execution Time Prediction of Functional Tests Using Graph Neural Networks
An Empirical Analysis of Microservices Systems Using Consumer-Driven Contract Testing
Dependency management bots in open-source systems—prevalence and adoption
Applying test case prioritization to software microbenchmarks
What's Wrong With My Benchmark Results? Studying Bad Practices in JMH Benchmarks
The Migration Journey Towards Microservices
Facing the giant: A grounded theory study of decision-making in microservices migrations
Function-as-a-Service Performance Evaluation: A Multivocal Literature Review
Emerging Trends, Challenges, and Experiences in DevOps and Microservice APIs
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
Current and Future Bots in Software Development
Cachematic – Automatic Invalidation in Application-Level Caching Systems
Performance Benchmarking of Infrastructure-as-a-Service (IaaS) Clouds with CloudWorkBench
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
PerformanceHat - Augmenting Source Code with Runtime Performance Traces in the IDE
Search-Based Scheduling of Experiments in Continuous Deployment
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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Showing 2 research projects
Truck Architecture for Functionality in the Cloud (TrAF-Cloud)
ImmeRSEd - Developer-Targeted Performance Engineering for Immersed Release and Software Engineers