Coverage-Based Debloating for Java Bytecode

Soto-Valero, César; Durieux, Thomas; Harrand, Nicolas; Baudry, Benoît

doi:10.48550/arxiv.2008.08401

Cited by 3 publications

(4 citation statements)

References 36 publications

(42 reference statements)

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“…Runtimes were not designed for serverless architectures and recent optimizations for Java [25] and .NET [62] achieve large speedups of up to 10×, though sometimes at the cost of more memory usage or larger deployment sizes. Debloating system stacks [36] and application dependencies [68] is another promising optimization motivated by large initialization overheads for applications with large dependency trees (e.g., App-E and App-J). Alternatively, serverless developers can select languages with lower runtime initialization overhead, such as Golang [46].…”

Section: Discussionmentioning

confidence: 99%

Let's Trace It: Fine-Grained Serverless Benchmarking using Synchronous and Asynchronous Orchestrated Applications

Scheuner¹,

Eismann²,

Talluri³

et al. 2022

Preprint

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Making serverless computing widely applicable requires detailed performance understanding. Although contemporary benchmarking approaches exist, they report only coarse results, do not apply distributed tracing, do not consider asynchronous applications, and provide limited capabilities for (root cause) analysis. Addressing this gap, we design and implement ServiBench, a serverless benchmarking suite. ServiBench (i) leverages synchronous and asynchronous serverless applications representative of production usage, (ii) extrapolates cloud-provider data to generate realistic workloads, (iii) conducts comprehensive, end-to-end experiments to capture application-level performance, (iv) analyzes results using a novel approach based on (distributed) serverless tracing, and (v) supports comprehensively serverless performance analysis. With ServiBench, we conduct comprehensive experiments on AWS, covering five common performance factors: median latency, cold starts, tail latency, scalability, and dynamic workloads. We find that the median end-to-end latency of serverless applications is often dominated not by function computation but by external service calls, orchestration, or trigger-based coordination. We release collected experimental data under FAIR principles and ServiBench as a tested, extensible open-source tool.

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Section: Discussionmentioning

confidence: 99%

Let's Trace It: Fine-Grained Serverless Benchmarking using Synchronous and Asynchronous Orchestrated Applications

Scheuner¹,

Eismann²,

Talluri³

et al. 2022

Preprint

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show abstract

“…Similarly, we observed that the number of bloated dependencies tends to grow over time, whether or not there is a need for it. In the last years, there is a recent resurgence of interest in debloating Java bytecode [5,14,20,28,29]. These tools remove Java bytecode using static and dynamic analysis.…”

Section: Related Workmentioning

confidence: 99%

A Longitudinal Analysis of Bloated Java Dependencies

Soto-Valero¹,

Durieux²,

Baudry³

2021

Preprint

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We study the evolution and impact of bloated dependencies in a single software ecosystem: Java/Maven. Bloated dependencies are third-party libraries that are packaged in the application binary but are not needed to run the application. We analyze the history of 435 Java projects. This historical data includes 48,469 distinct dependencies, which we study across a total of 31,515 versions of Maven dependency trees. Bloated dependencies steadily increase over time, and 89.2 % of the direct dependencies that are bloated remain bloated in all subsequent versions of the studied projects. This empirical evidence suggests that developers can safely remove a bloated dependency. We further report novel insights regarding the unnecessary maintenance efforts induced by bloat. We find that 22 % of dependency updates performed by developers are made on bloated dependencies, and that Dependabot suggests a similar ratio of updates on bloated dependencies.

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“…The test-suite of the clients can also be used as further validation of modifications performed on the library. This is the type of usage that we leverage in our recent work [6], where we debloat libraries and verify that the compilation and the execution of their clients' tests are not affected. Hence, having information regarding library usage by clients is useful for validating program transformation since it provides dynamic data that helps to overcome the limitations of static analysis in Java.…”

Section: A Pairs Of Libraries and Clientsmentioning

confidence: 99%

“…In general, many program analyses require a list of compilable and testable software packages, which we provide with DUETS. The dataset also supports more specific use cases, such as analyzing the API usage by the clients of a particular library [5], or debloating based on dynamic analysis [6], [7]. We provide a framework with the dataset.…”

Section: Introductionmentioning

confidence: 99%

DUETS: A Dataset of Reproducible Pairs ofJava Library-Clients

Durieux,

Soto-Valero,

Baudry

2021

Preprint

Self Cite

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Software engineering researchers look for software artifacts to study their characteristics or to evaluate new techniques. In this paper, we introduce DUETS, a new dataset of software libraries and their clients. This dataset can be exploited to gain many different insights, such as API usage, usage inputs, or novel observations about the test suites of clients and libraries. DUETS is meant to support both static and dynamic analysis. This means that the libraries and the clients compile correctly, they are executable and their test suites pass. The dataset is composed of open-source projects that have more than five stars on GitHub. The final dataset contains 395 libraries and 2,874 clients. Additionally, we provide the raw data that we use to create this dataset, such as 34,560 pom.xml files or the complete file list from 34,560 projects. This dataset can be used to study how libraries are used by their clients or as a list of software projects that successfully build. The client's test suite can be used as an additional verification step for code transformation techniques that modify the libraries.

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Coverage-Based Debloating for Java Bytecode

Cited by 3 publications

References 36 publications

Let's Trace It: Fine-Grained Serverless Benchmarking using Synchronous and Asynchronous Orchestrated Applications

Let's Trace It: Fine-Grained Serverless Benchmarking using Synchronous and Asynchronous Orchestrated Applications

A Longitudinal Analysis of Bloated Java Dependencies

DUETS: A Dataset of Reproducible Pairs ofJava Library-Clients

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