Update reference to publication

docs/concepts/benchmarks-and-executions.md

@@ -42,3 +42,7 @@ In contrast to benchmarks, execution have a life-cycle. They can be planned,
 executed, or aborted. Each execution of a benchmark is represented by an
 individual entity. This supports repeatability as executions can be archived
 and shared.
+
+## Further Reading
+
+S. Henning and W. Hasselbring. “[A Configurable Method for Benchmarking Scalability of Cloud-Native Applications](https://doi.org/10.1007/s10664-022-10162-1)”. In: *Empirical Software Engineering* 27. 2022. DOI: [10.1007/s10664-022-10162-1](https://doi.org/10.1007/s10664-022-10162-1).

docs/concepts/metrics.md

@@ -64,5 +64,4 @@ $$
 
 ## Further Reading
 
-S. Henning and W. Hasselbring. “[How to Measure Scalability of Distributed Stream Processing Engines?](https://research.spec.org/icpe_proceedings/2021/companion/p85.pdf)” In: International Conference on Performance Engineering. 2021. DOI: [10.1145/3447545.3451190](https://doi.org/10.1145/3447545.3451190).
-<!-- TODO replace with EMSE paper one day-->
+S. Henning and W. Hasselbring. “[A Configurable Method for Benchmarking Scalability of Cloud-Native Applications](https://doi.org/10.1007/s10664-022-10162-1)”. In: *Empirical Software Engineering* 27. 2022. DOI: [10.1007/s10664-022-10162-1](https://doi.org/10.1007/s10664-022-10162-1).

docs/concepts/search-strategies.md

@@ -35,4 +35,6 @@ The lower bound restriction is a search strategy that uses the results of alread
 For the resource demand, it starts searching (with another strategy) beginning from the minimal required resources of all lower load intensities.
 The lower bound restriction is based on the assumption that with increasing load intensity, the resource demand never decreases.
 
-<!-- further reading in EMSE paper -->
+## Further Reading
\ No newline at end of file
+
+S. Henning and W. Hasselbring. “[A Configurable Method for Benchmarking Scalability of Cloud-Native Applications](https://doi.org/10.1007/s10664-022-10162-1)”. In: *Empirical Software Engineering* 27. 2022. DOI: [10.1007/s10664-022-10162-1](https://doi.org/10.1007/s10664-022-10162-1).

docs/publications.md

@@ -8,8 +8,7 @@ nav_order: 9
 
 Below you can find a list of publications that are directly related to Theodolite:
 
-* S. Henning and W. Hasselbring. “A Configurable Method for Benchmarking Scalability of Cloud-Native Applications”. In: *Empirical Software Engineering*. 2022. In press. DOI: 10.
-1007/s10664-022-10162-1.
+* S. Henning and W. Hasselbring. “[A Configurable Method for Benchmarking Scalability of Cloud-Native Applications](https://doi.org/10.1007/s10664-022-10162-1)”. In: *Empirical Software Engineering* 27. 2022. DOI: [10.1007/s10664-022-10162-1](https://doi.org/10.1007/s10664-022-10162-1).
 * T. Pfandzelter, S. Henning, T. Schirmer, W. Hasselbring, and D. Bermbach. “[Streaming vs. Functions: A Cost Perspective on Cloud Event Processing](https://arxiv.org/pdf/2204.11509.pdf)”. In: *IEEE International Conference on Cloud Engineering*. 2022. In press.
 * S. Henning and W. Hasselbring. “Demo Paper: Benchmarking Scalability of Cloud-Native Applications with Theodolite”. In: *IEEE International Conference on Cloud Engineering*. 2022. In press.
 * S. Henning, B. Wetzel, and W. Hasselbring. “[Reproducible Benchmarking of Cloud-Native Applications With the Kubernetes Operator Pattern](http://ceur-ws.org/Vol-3043/short5.pdf)”. In: *Symposium on Software Performance*. 2021.