Keeping Master Green at Scale

Ananthanarayanan, Sundaram; Ardekani, Masoud Saeida; Haenikel, Denis; Varadarajan, Balaji; Soriano, Simon; Patel, Dhaval; Adl-Tabatabai, Ali-Reza

doi:10.1145/3302424.3303970

Cited by 7 publications

(2 citation statements)

References 31 publications

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“…In software development, oftentimes there is a massive interdependence between files, e.g. external libraries, large projects, etc., and, sometimes, these are the source of many introduced regressions [5]. These interdependencies are paper-thin and burdensome to find manually.…”

Section: Entity Representationmentioning

confidence: 99%

Neural Network Embeddings for Test Case Prioritization

Lousada,

Ribeiro

2020

Preprint

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In modern software engineering, Continuous Integration (CI) has become an indispensable step towards systematically managing the life cycles of software development. Large companies struggle with keeping the pipeline updated and operational, in useful time, due to the large amount of changes and addition of features, that build on top of each other and have several developers, working on different platforms. Associated with such software changes, there is always a strong component of Testing. As teams and projects grow, exhaustive testing quickly becomes inhibitive, becoming adamant to select the most relevant test cases earlier, without compromising software quality. We have developed a new tool called Neural Network Embeeding for Test Case Prioritization (NNE-TCP) is a novel Machine-Learning (ML) framework that analyses which files were modified when there was a test status transition and learns relationships between these files and tests by mapping them into multidimensional vectors and grouping them by similarity. When new changes are made, tests that are more likely to be linked to the files modified are prioritized, reducing the resources needed to find newly introduced faults. Furthermore, NNE-TCP enables entity visualization in lowdimensional space, allowing for other manners of grouping files and tests by similarity and to reduce redundancies. By applying NNE-TCP, we show for the first time that the connection between modified files and tests is relevant and competitive relative to other traditional methods.

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Section: Entity Representationmentioning

confidence: 99%

Neural Network Embeddings for Test Case Prioritization

Lousada,

Ribeiro

2020

Preprint

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“…code layout optimization techniques are sensitive to profile quality [55], they work exceptionally well in practice [27,33,47,100,106,107]. Profiles for data center applications change slowly over several weeks [33] while data center operators profile and recompile applications multiple times a day [21,33,106,107]. Consequently, these automated techniques have ample opportunity to adapt to changing application profiles and are widely used in today's data centers [27,33,47,106,107].…”

Section: Introductionmentioning

confidence: 99%

Thermometer

Song

Khan

Shahri

et al. 2022

Proceedings of the 49th Annual International Symposium on Computer Architecture

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Modern processors employ a decoupled frontend with Fetch Directed Instruction Prefetching (FDIP) to avoid frontend stalls in data center applications. However, the large branch footprint of data center applications precipitates frequent Branch Target Buffer (BTB) misses that prohibit FDIP from eliminating more than 40% of all frontend stalls. We find that the state-of-the-art BTB optimization techniques (e.g., BTB prefetching and replacement mechanisms) cannot eliminate these misses due to their inadequate understanding of branch reuse behavior in data center applications.In this paper, we first perform a comprehensive characterization of the branch behavior of data center applications, and determine that identifying optimal BTB replacement decisions requires considering both transient and holistic (i.e., across the entire execution) branch behavior. We then present Thermometer, a novel BTB replacement technique that realizes the holistic branch behavior via a profile-guided analysis. Based on the collected profile, Thermometer generates useful BTB replacement hints that the underlying hardware can leverage. We evaluate Thermometer using 13 widelyused data center applications and demonstrate that it provides an average speedup of 8.7% (0.4%-64.9%) while outperforming the state-of-the-art BTB replacement techniques by 5.6× (on average, the best performing prior work achieves 1.5% speedup). We also demonstrate that Thermometer achieves a performance speedup that is, on average, 83.6% of the speedup achieved by the optimal BTB replacement policy.

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