546,382 research outputs found

    Input-Sensitive Performance Testing

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    One goal of performance testing is to find specific test input data for exposing performance bottlenecks and identify the methods responsible for these performance bottlenecks. A big and important challenges of performance testing is how to deeply understand the performance behaviors of a non-trivial software system in terms of test input data to properly select the specific test input values for finding the problematic methods. Thus, we propose this research program to automatically analyze performance behaviors in software and link these behaviors with test input data for selecting the specific ones that can expose performance bottlenecks. In addition, this research further examines the corresponding execution traces of selected inputs for targeting the problematic methods

    Automatic Performance Testing using Input-Sensitive Profiling

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    During performance testing, software engineers commonly perform application profiling to analyze an application\u27s traces with different inputs to understand performance behaviors, such as time and space consumption. However, a non-trivial application commonly has a large number of inputs, and it is mostly manual to identify the specific inputs leading to performance bottlenecks. Thus, it is challenge is to automate profiling and find these specific inputs. To solve these problems, we propose novel approaches, FOREPOST, GA-Prof and PerfImpact, which automatically profile applications for finding the specific combinations of inputs triggering performance bottlenecks, and further analyze the corresponding traces to identify problematic methods. Specially, our approaches work in two different types of real-world scenarios of performance testing: i) a single-version scenario, in which performance bottlenecks are detected in a single software release, and ii) a two-version scenario, in which code changes responsible for performance regressions are detected by considering two consecutive software releases

    Environmental Effects On Drosophila Brain Development And Learning

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    Brain development and behavior are sensitive to a variety of environmental influences including social interactions and physicochemical stressors. Sensory input in situ is a mosaic of both enrichment and stress, yet little is known about how multiple environmental factors interact to affect brain anatomical structures, circuits and cognitive function. In this study, we addressed these issues by testing the individual and combined effects of sub-adult thermal stress, larval density and early-adult living spatial enrichment on brain anatomy and olfactory associative learning in adult Drosophila melanogaster. In response to heat stress, the mushroom bodies (MBs) were the most volumetrically impaired among all of the brain structures, an effect highly correlated with reduced odor learning performance. However, MBs were not sensitive to either larval culture density or early-adult living conditions. Extreme larval crowding reduced the volume of the antennal lobes, optic lobes and central complex. Neither larval crowding nor early-adult spatial enrichment affected olfactory learning. These results illustrate that various brain structures react differently to environmental inputs, and that MB development and learning are highly sensitive to certain stressors (pre-adult hyperthermia) and resistant to others (larval crowding). © 2018. Published by The Company of Biologists Ltd

    A microgravity isolation mount

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    The design and preliminary testing of a system for isolating microgravity sensitive payloads from spacecraft vibrational and impulsive disturbances is discussed. The Microgravity Isolation Mount (MGIM) concept consists of a platform which floats almost freely within a limited volume inside the spacecraft, but which is constrained to follow the spacecraft in the long term by means of very weak springs. The springs are realized magnetically and form part of a six degree of freedom active magnetic suspension system. The latter operates without any physical contact between the spacecraft and the platform itself. Power and data transfer is also performed by contactless means. Specifications are given for the expected level of input disturbances and the tolerable level of platform acceleration. The structural configuration of the mount is discussed and the design of the principal elements, i.e., actuators, sensors, control loops and power/data transfer devices are described. Finally, the construction of a hardware model that is being used to verify the predicted performance of the MGIM is described

    Identifying Model Weakness with Adversarial Examiner

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    Machine learning models are usually evaluated according to the average case performance on the test set. However, this is not always ideal, because in some sensitive domains (e.g. autonomous driving), it is the worst case performance that matters more. In this paper, we are interested in systematic exploration of the input data space to identify the weakness of the model to be evaluated. We propose to use an adversarial examiner in the testing stage. Different from the existing strategy to always give the same (distribution of) test data, the adversarial examiner will dynamically select the next test data to hand out based on the testing history so far, with the goal being to undermine the model's performance. This sequence of test data not only helps us understand the current model, but also serves as constructive feedback to help improve the model in the next iteration. We conduct experiments on ShapeNet object classification. We show that our adversarial examiner can successfully put more emphasis on the weakness of the model, preventing performance estimates from being overly optimistic.Comment: To appear in AAAI-2

    Effects of Local Latency on Games

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    Video games are a major type of entertainment for millions of people, and feature a wide variety genres. Many genres of video games require quick reactions, and in these games it is critical for player performance and player experience that the game is responsive. One of the major contributing factors that can make games less responsive is local latency — the total delay between input and a resulting change to the screen. Local latency is produced by a combination of delays from input devices, software processing, and displays. Due to latency, game companies spend considerable time and money play-testing their games to ensure the game is both responsive and that the in-game difficulty is reasonable. Past studies have made it clear that local latency negatively affects both player performance and experience, but there is still little knowledge about local latency’s exact effects on games. In this thesis, we address this problem by providing game designers with more knowledge about local latency’s effects. First, we performed a study to examine latency’s effects on performance and experience for popular pointing input devices used with games. Our results show significant differences between devices based on the task and the amount of latency. We then provide design guidelines based on our findings. Second, we performed a study to understand latency’s effects on ‘atoms’ of interaction in games. The study varied both latency and game speed, and found game speed to affect a task’s sensitivity to latency. Third, we used our findings to build a model to help designers quickly identify latency-sensitive game atoms, thus saving time during play-testing. We built and validated a model that predicts errors rates in a game atom based on latency and game speed. Our work helps game designers by providing new insight into latency’s varied effects and by modelling and predicting those effect
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