JBrainy: Micro-benchmarking Java Collections with Interference
| Autor: | Emma Söderberg, Noric Couderc, Christoph Reichenbach |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Database
Java Computer science Process (engineering) business.industry Contrast (statistics) 020207 software engineering 02 engineering and technology Benchmarking computer.software_genre Data structure Task (project management) Software 020204 information systems Java collections framework 0202 electrical engineering electronic engineering information engineering business computer computer.programming_language |
| Zdroj: | ICPE Companion |
| Popis: | Software developers use collection data structures extensively and are often faced with the task of picking which collection to use. Choosing an inappropriate collection can have major negative impact on runtime performance. However, choosing the right collection can be difficult since developers are faced with many possibilities, which often appear functionally equivalent. One approach to assist developers in this decision-making process is to micro-benchmark data-structures in order to provide performance insights. In this paper, we present results from experiments on Java collections (maps, lists, and sets) using our tool JBrainy, which synthesises micro-benchmarks with sequences of random method calls. We compare our results to the results of a previous experiment on Java collections that uses a micro-benchmarking approach focused on single methods. Our results support previous results for lists, in that we found ArrayList to yield the best running time in 90% of our benchmarks. For sets, we found LinkedHashSet to yield the best performance in 78% of the benchmarks. In contrast to previous results, we found TreeMap and LinkedHashMap to yield better runtime performance than HashMap in 84% of cases. |
| Databáze: | OpenAIRE |
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