Understanding and detecting real-world performance bugs

JinGuoliang,; SongLinhai,; ShiXiaoming,; ScherpelzJoel,; Lu, Shan

doi:10.1145/2345156.2254075

Cited by 145 publications

(170 citation statements)

References 52 publications

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“…Performance problems are often related to expensive loops [23]. As described in Section 4.3.2, in FDD we predict the outcome of changes in code by utilizing existing data to infer feedback for new software artifacts.…”

Section: Critical Loop Predictionmentioning

confidence: 99%

Runtime metric meets developer - Building better cloud applications using feedback

Cito

Leitner

Gall

et al. 2015

Preprint

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A unifying theme of many ongoing trends in software engineering is a blurring of the boundaries between building and operating software products. In this paper, we explore what we consider to be the logical next step in this succession: integrating runtime monitoring data from production deployments of the software into the tools developers utilize in their daily workflows (i.e., IDEs) to enable tighter feedback loops. We refer to this notion as feedback-driven development (FDD).This more abstract FDD concept can be instantiated in various ways, ranging from IDE plugins that implement feedback-driven refactoring and code optimization to plugins that predict performance and cost implications of code changes prior to even deploying the new version of the software. We demonstrate existing proof-of-concept realizations of these ideas and illustrate our vision of the future of FDD and cloud-based software development in general. Further, we discuss the major challenges that need to be solved before FDD can achieve mainstream adoption.

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Section: Critical Loop Predictionmentioning

confidence: 99%

Runtime metric meets developer - Building better cloud applications using feedback

Cito

Leitner

Gall

et al. 2015

Preprint

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“…Different categories could be rated with 5 choices ranging from Never to Very often. The categories have been chosen based on existing surveys from related domains [7,5,13,8]. The results in Figure 2c indicate that algorithmic faults are the most frequent cause for failures, followed by resource leaks (not limited to memory) and skippable computations.…”

Section: Induced Faultsmentioning

confidence: 99%

A Data Set for Fault Detection Research on Component-Based Robotic Systems

Wienke

Borgsen

Wrede

2016

Towards Autonomous Robotic Systems

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Abstract. Fault detection and identification methods (FDI) are an important aspect for ensuring consistent behavior of technical systems. In robotics FDI promises to improve the autonomy and robustness. Existing FDI research in robotics mostly focused on faults in specific areas, like sensor faults. While there is FDI research also on the overarching software system, common data sets to benchmark such solutions do not exist. In this paper we present a data set for FDI research on robot software systems to bridge this gap. We have recorded an HRI scenario with our RoboCup@Home platform and induced diverse empirically grounded faults using a novel, structured method. The recordings include the complete event-based communication of the system as well as detailed performance counters for all system components and exact ground-truth information on the induced faults. The resulting data set is a challenging benchmark for FDI research in robotics which is publicly available.

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“…through crashing important components, but instead slowly degrade its perceived or computational performance and often only occur on special inputs. Hence, they are much harder to detect and easily missed during short testing cycles in active development work [9]. Also, these non-catastrophic issues have a higher potential for being recovered at runtime and therefore are the most valuable ones to detect with FDI methods.…”

Section: Target Systems and Assumptionsmentioning

confidence: 99%

Autonomous fault detection for performance bugs in component-based robotic systems

Wienke

Wrede

2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-We present a novel fault detection method for application in component-based robotic systems. In contrast to existing work, our method specifically addresses faults in the software system of the robot using a data-driven methodology which exploits the inter-process communication of the system. This enables an application of the approach without expert knowledge or availability of complex software models. We specifically focus on performance bugs, which slowly degrade the performance of the system and are thereby harder to detect but also most valuable for automatic recovery. Using a data set recorded on a RoboCup@Home platform we demonstrate the performance and applicability of our method and analyze the types of faults that can be detected by the method.

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Understanding and detecting real-world performance bugs

Cited by 145 publications

References 52 publications

Runtime metric meets developer - Building better cloud applications using feedback

Runtime metric meets developer - Building better cloud applications using feedback

A Data Set for Fault Detection Research on Component-Based Robotic Systems

Autonomous fault detection for performance bugs in component-based robotic systems

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