Towards Safer Heuristics With XPlain
Many problems that cloud operators solve are computationally expensive, and operators often use heuristic algorithms (that are faster and scale better than optimal) to solve them more efficiently. Heuristic analyzers enable operators to find when and by how much their heuristics underperform. However, these tools do not provide enough detail for operators to mitigate the heuristic's impact in practice: they only discover a single input instance that causes the heuristic to underperform (and not the full set) and they do not explain why.
Authors
- Santiago Segarra (ORCID: https://orcid.org/0000-0002-8408-9633)
- Beibin Li (ORCID: https://orcid.org/0000-0002-4426-3449)
- Pooria Namyar (ORCID: https://orcid.org/0000-0001-7704-5440)
- Solal Pirelli (ORCID: https://orcid.org/0009-0003-4336-1316)
- Behnaz Arzani (ORCID: https://orcid.org/0000-0003-2234-3250)
- Ryan Beckett (ORCID: https://orcid.org/0000-0001-7844-2026)
- Siva Kesava Reddy Kakarla (ORCID: https://orcid.org/0009-0006-2694-4685)
- Pantea Karimi
Institutions
- University of Southern California (US)
- California Southern University (US)
- Moscow Institute of Thermal Technology (RU)
- Microsoft Research (United Kingdom) (GB)
- Rice University (US)
Publication Details
- Published
- 2024-11-11
- DOI
- https://doi.org/10.1145/3696348.3696884
- Primary Topic
- Software Testing and Debugging Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00