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We show that optomechanical systems can be primary pressure sensors with uncertainty as low as 1.1 % of reading via comparison with a pressure transfer standard. Our silicon nitride and silicon carbide sensors are short-term and long-term stable, displaying Allan deviations compatible with better than 1 % precision and baseline drift significantly lower than the transfer standard. We also investigate the performance of optomechanical devices as calibrated gauges, finding that they can achieve total uncertainty less than 1 %. The calibration procedure also yields the thin-film density of our sensors with state-of-the-art precision, aiding development of other calibration-free optomechanical sensors. Our results demonstrate that optomechanical pressure sensors can achieve accuracy, precision, and drift sufficient to replace high performance legacy gauges., Comment: 15 pages, 7 figures

The Linux kernel is highly-configurable, with a build system that takes a configuration file as input and automatically tailors the source code accordingly. Configurability, however, complicates testing, because different configuration options lead to the inclusion of different code fragments. With thousands of patches received per month, Linux kernel maintainers employ extensive automated continuous integration testing. To attempt patch coverage, i.e., taking all changed lines into account, current approaches either use configuration files that maximize total statement coverage or use multiple randomly-generated configuration files, both of which incur high build times without guaranteeing patch coverage. To achieve patch coverage without exploding build times, we propose krepair, which automatically repairs configuration files that are fast-building but have poor patch coverage to achieve high patch coverage with little effect on build times. krepair works by discovering a small set of changes to a configuration file that will ensure patch coverage, preserving most of the original configuration file's settings. Our evaluation shows that, when applied to configuration files with poor patch coverage on a statistically-significant sample of recent Linux kernel patches, krepair achieves nearly complete patch coverage, 98.5% on average, while changing less than 1.53% of the original default configuration file in 99% of patches, which keeps build times 10.5x faster than maximal configuration files.

Proper naming of methods can make program code easier to understand, and thus enhance software maintainability. Yet, developers may use inconsistent names due to poor communication or a lack of familiarity with conventions within the software development lifecycle. To address this issue, much research effort has been invested into building automatic tools that can check for method name inconsistency and recommend consistent names. However, existing datasets generally do not provide precise details about why a method name was deemed improper and required to be changed. Such information can give useful hints on how to improve the recommendation of adequate method names. Accordingly, we construct a sample method-naming benchmark, ReName4J, by matching name changes with code reviews. We then present an empirical study on how state-of-the-art techniques perform in detecting or recommending consistent and inconsistent method names based on ReName4J. The main purpose of the study is to reveal a different perspective based on reviewed names rather than proposing a complete benchmark. We find that the existing techniques underperform on our review-driven benchmark, both in inconsistent checking and the recommendation. We further identify potential biases in the evaluation of existing techniques, which future research should consider thoroughly.

The SZZ algorithm is used to connect bug-fixing commits to the earlier commits that introduced bugs. This algorithm has many applications and many variants have been devised. However, there are some types of commits that cannot be traced by the SZZ algorithm, referred to as "ghost commits". The evaluation of how these ghost commits impact the SZZ algorithm remains limited. Moreover, these algorithms have been evaluated on datasets created by software engineering researchers from information in bug trackers and version controlled histories. Since Oct 2013, the Linux kernel developers have started labelling bug-fixing patches with the commit identifiers of the corresponding bug-inducing commit(s) as a standard practice. As of v6.1-rc5, 76,046 pairs of bug-fixing patches and bug-inducing commits are available. This provides a unique opportunity to evaluate the SZZ algorithm on a large dataset that has been created and reviewed by project developers, entirely independently of the biases of software engineering researchers. In this paper, we apply six SZZ algorithms to 76,046 pairs of bug-fixing patches and bug-introducing commits from the Linux kernel. Our findings reveal that SZZ algorithms experience a more significant decline in recall on our dataset (13.8%) as compared to prior findings reported by Rosa et al., and the disparities between the individual SZZ algorithms diminish. Moreover, we find that 17.47% of bug-fixing commits are ghost commits. Finally, we propose Tracing-Commit SZZ (TC-SZZ), that traces all commits in the change history of lines modified or deleted in bug-fixing commits. Applying TC-SZZ to all failure cases, excluding ghost commits, we found that TC-SZZ could identify 17.7% of them. Our further analysis found that 34.6% of bug-inducing commits were in the function history, 27.5% in the file history (but not in the function history), and..., Comment: This article has been accepted for publication in IEEE Transactions on Software Engineering

We report three orders of magnitude optical cooling of the fundamental torsional mode of a 5 mm-long, 550 nm diameter optical nanofiber. The rotation of the nanofiber couples to the polarization of guided laser fields. We use a weak laser probe to monitor the rotation, and use feedback to modulate the polarization of an auxiliary drive laser providing torque. Our results present a tool for the optomechanical control of large-scale torsional resonators, with metrological applications and potential implications for studying macroscopic objects in quantum states.

Photonic crystal (PhC) membranes patterned with sub-wavelength periods offer a unique combination of high reflectivity, low mass, and high mechanical quality factor. We demonstrate a PhC membrane that we use as one mirror of a Fabry-Perot cavity with finesse as high as $F=35,000(500)$, corresponding to a record high PhC reflectivity of $R=0.999835(6)$. The fundamental mechanical frequency is 426 kHz, more than twice the optical linewidth, placing it firmly in the resolved-sideband regime. The mechanical quality factor in vacuum is $Q=1.1(1)\times 10^6$, allowing us to achieve values of the single-photon cooperativity as high as ${\cal C}_0=6.6\times10^{-3}$. We easily see optomechanical bistability as hysteresis in the cavity transmission. As the input power is raised well beyond the bistability threshold, dynamical backaction induces strong mechanical oscillation above 1~MHz, even in the presence of air damping. This platform will facilitate advances in optomechanics, precision sensing, and applications of optomechanically-induced bistability., Comment: 10 pages, 5 figures

Android operating system (OS) is often updated, where each new version may involve API deprecation. Usages of deprecated APIs in Android apps need to be updated to ensure the apps' compatibility with the old and new versions of Android OS. In this work, we propose AndroEvolve, an automated tool to update usages of deprecated Android APIs, that addresses the limitations of the state-of-the-art tool, CocciEvolve. AndroEvolve utilizes data flow analysis to solve the problem of out-of-method-boundary variables, and variable denormalization to remove the temporary variables introduced by CocciEvolve. We evaluated the accuracy of AndroEvolve using a dataset of 360 target files and 20 deprecated Android APIs, where AndroEvolve is able to produce 319 correct updates, compared to CocciEvolve which only produces 249 correct updates. We also evaluated the readability of AndroEvolve's update results using a manual and an automatic evaluation. Both evaluations demonstrated that the code produced by AndroEvolve has higher readability than CocciEvolve's. A video demonstration of AndroEvolve is available at https://youtu.be/siU0tuMITXI.

The Android operating system is frequently updated, with each version bringing a new set of APIs. New versions may involve API deprecation; Android apps using deprecated APIs need to be updated to ensure the apps' compatibility withold and new versions of Android. Updating deprecated APIs is a time-consuming endeavor. Hence, automating the updates of Android APIs can be beneficial for developers. CocciEvolve is the state-of-the-art approach for this automation. However, it has several limitations, including its inability to resolve out-of-method-boundary variables and the low code readability of its update due to the addition of temporary variables. In an attempt to further improve the performance of automated Android API update, we propose an approach named AndroEvolve, which addresses the limitations of CocciEvolve through the addition of data flow analysis and variable name denormalization. Data flow analysis enables AndroEvolve to resolve the value of any variable within the file scope. Variable name denormalization replaces temporary variables that may present in the CocciEvolve update with appropriate values in the target file. We have evaluated the performance of AndroEvolve and the readability of its updates on 360 target files. AndroEvolve produces 26.90% more instances of correct updates compared to CocciEvolve. Moreover, our manual and automated evaluation shows that AndroEvolve updates are more readable than CocciEvolve updates.

Due to the rise of AI applications, machine learning libraries have become far more accessible, with Python being the most common programming language to write them. Machine learning libraries tend to be updated periodically, which may deprecate existing APIs, making it necessary for developers to update their usages. However, updating usages of deprecated APIs are typically not a priority for developers, leading to widespread usages of deprecated APIs which expose library users to vulnerability issues. In this paper, we built a tool to automate these updates. We first conducted an empirical study to seek a better understanding on how updates of deprecated machine-learning API usages in Python can be done. The study involved a dataset of 112 deprecated APIs from Scikit-Learn, TensorFlow, and PyTorch. We found dimensions of deprecated API migration related to its update operation (i.e., the required operation to perform the migration), API mapping (i.e., the number of deprecated and its corresponding updated APIs),and context dependency (i.e., whether we need to consider surrounding contexts when performing the migration). Guided by the findings on our empirical study, we created MLCatchUp, a tool to automate the update of Python deprecated API usage that automatically infers the API migration transformation through comparison of the deprecated and updated API signatures. These transformations are expressed in a Domain Specific Language (DSL). We evaluated MLCatchUp using test dataset containing 258 files with 514 API usages that we collected from public GitHub repositories. In this evaluation, MLCatchUp achieves a precision of 86.19%. We further improve the precision of MLCatchUp by adding a feature that allows it to accept additional user input to specify the transformation constraints in the DSL for context-dependent API migration, where MLCatchUp achieves a precision of 93.58%.

Due to the deprecation of APIs in the Android operating system,developers have to update usages of the APIs to ensure that their applications work for both the past and current versions of Android.Such updates may be widespread, non-trivial, and time-consuming. Therefore, automation of such updates will be of great benefit to developers. AppEvolve, which is the state-of-the-art tool for automating such updates, relies on having before- and after-update examples to learn from. In this work, we propose an approach named CocciEvolve that performs such updates using only a single after-update example. CocciEvolve learns edits by extracting the relevant update to a block of code from an after-update example. From preliminary experiments, we find that CocciEvolve can successfully perform 96 out of 112 updates, with a success rate of 85%., Comment: 5 pages, 8 figures. Accepted in The International Conference on Program Comprehension (ICPC) 2020, ERA Track

Existing work on software patches often use features specific to a single task. These works often rely on manually identified features, and human effort is required to identify these features for each task. In this work, we propose CC2Vec, a neural network model that learns a representation of code changes guided by their accompanying log messages, which represent the semantic intent of the code changes. CC2Vec models the hierarchical structure of a code change with the help of the attention mechanism and uses multiple comparison functions to identify the differences between the removed and added code. To evaluate if CC2Vec can produce a distributed representation of code changes that is general and useful for multiple tasks on software patches, we use the vectors produced by CC2Vec for three tasks: log message generation, bug fixing patch identification, and just-in-time defect prediction. In all tasks, the models using CC2Vec outperform the state-of-the-art techniques.

Linux kernel stable versions serve the needs of users who value stability of the kernel over new features. The quality of such stable versions depends on the initiative of kernel developers and maintainers to propagate bug fixing patches to the stable versions. Thus, it is desirable to consider to what extent this process can be automated. A previous approach relies on words from commit messages and a small set of manually constructed code features. This approach, however, shows only moderate accuracy. In this paper, we investigate whether deep learning can provide a more accurate solution. We propose PatchNet, a hierarchical deep learning-based approach capable of automatically extracting features from commit messages and commit code and using them to identify stable patches. PatchNet contains a deep hierarchical structure that mirrors the hierarchical and sequential structure of commit code, making it distinctive from the existing deep learning models on source code. Experiments on 82,403 recent Linux patches confirm the superiority of PatchNet against various state-of-the-art baselines, including the one recently-adopted by Linux kernel maintainers.

We demonstrate the tunable quantum beat of single photons through the co-development of core nonlinear nanophotonic technologies for frequency-domain manipulation of quantum states in a common physical platform. Spontaneous four-wave mixing in a nonlinear resonator is used to produce non-degenerate, quantum-correlated photon pairs. One photon from each pair is then frequency shifted, without degradation of photon statistics, using four-wave mixing Bragg scattering in a second nonlinear resonator. Fine tuning of the applied frequency shift enables tunable quantum interference of the two photons as they are impinged on a beamsplitter, with an oscillating signature that depends on their frequency difference. Our work showcases the potential of nonlinear nanophotonic devices as a valuable resource for photonic quantum information science.

Single self-assembled InAs/GaAs quantum dots are promising bright sources of indistinguishable photons for quantum information science. However, their distribution in emission wavelength, due to inhomogeneous broadening inherent to their growth, has limited the ability to create multiple identical sources. Quantum frequency conversion can overcome this issue, particularly if implemented using scalable chip-integrated technologies. Here, we report the first demonstration of quantum frequency conversion of a quantum dot single-photon source on a silicon nanophotonic chip. Single photons from a quantum dot in a micropillar cavity are shifted in wavelength with an on-chip conversion efficiency of approximately 12 %, limited by the linewidth of the quantum dot photons. The intensity autocorrelation function g(2)(tau) for the frequency-converted light is antibunched with g(2)(0) = 0.290 +/- 0.030, compared to the before-conversion value g(2)(0) = 0.080 +/- 0.003. We demonstrate the suitability of our frequency conversion interface as a resource for quantum dot sources by characterizing its effectiveness across a wide span of input wavelengths (840 nm to 980 nm), and its ability to achieve tunable wavelength shifts difficult to obtain by other approaches., Comment: Main text + supplementary information

This work proposes PatchNet, an automated tool based on hierarchical deep learning for classifying patches by extracting features from commit messages and code changes. PatchNet contains a deep hierarchical structure that mirrors the hierarchical and sequential structure of a code change, differentiating it from the existing deep learning models on source code. PatchNet provides several options allowing users to select parameters for the training process. The tool has been validated in the context of automatic identification of stable-relevant patches in the Linux kernel and is potentially applicable to automate other software engineering tasks that can be formulated as patch classification problems. A video demonstrating PatchNet is available at https://goo.gl/CZjG6X. The PatchNet implementation is available at https://github.com/hvdthong/PatchNetTool.

Single self-assembled InAs/GaAs quantum dots are a promising solid-state quantum technology, with which vacuum Rabi splitting, single-photon-level nonlinearities, and bright, pure, and indistinguishable single-photon generation having been demonstrated. For such achievements, nanofabrication is used to create structures in which the quantum dot preferentially interacts with strongly-confined optical modes. An open question is the extent to which such nanofabrication may also have an adverse influence, through the creation of traps and surface states that could induce blinking, spectral diffusion, and dephasing. Here, we use photoluminescence imaging to locate the positions of single InAs/GaAs quantum dots with respect to alignment marks with < 5 nm uncertainty, allowing us to measure their behavior before and after fabrication. We track the quantum dot emission linewidth and photon statistics as a function of distance from an etched surface, and find that the linewidth is significantly broadened (up to several GHz) for etched surfaces within a couple hundred nanometers of the quantum dot. However, we do not observe appreciable reduction of the quantum dot radiative efficiency due to blinking. We also show that atomic layer deposition can stabilize spectral diffusion of the quantum dot emission, and partially recover its linewidth., Comment: 11 pages, 8 figures