Estimating software testability can crucially assist software managers to optimize test budgets and software quality. In this paper, we propose a new approach that radically differs from the traditional approach of pursuing testability measurements based on software metrics, e.g., the size of the code or the complexity of the designs. Our approach exploits automatic test generation and mutation analysis to quantify the evidence about the relative hardness of developing effective test cases. In the paper, we elaborate on the intuitions and the methodological choices that underlie our proposal for estimating testability, introduce a technique and a prototype that allows for concretely estimating testability accordingly, and discuss our findings out of a set of experiments in which we compare the performance of our estimations both against and in combination with traditional software metrics. The results show that our testability estimates capture a complementary dimension of testability that can be synergistically combined with approaches based on software metrics to improve the accuracy of predictions.
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This paper proposes a novel framework integrating the principles of active stereo in standard passive camera systems without a physical pattern projector. We virtually project a pattern over the left and right images according to the sparse measurements obtained from a depth sensor. Any such devices can be seamlessly plugged into our framework, allowing for the deployment of a virtual active stereo setup in any possible environment, overcoming the limitation of pattern projectors, such as limited working range or environmental conditions. Experiments on indoor/outdoor datasets, featuring both long and close-range, support the seamless effectiveness of our approach, boosting the accuracy of both stereo algorithms and deep networks.
We present a distributed conjugate gradient method for distributed optimization problems, where each agent computes an optimal solution of the problem locally without any central computation or coordination, while communicating with its immediate, one-hop neighbors over a communication network. Each agent updates its local problem variable using an estimate of the average conjugate direction across the network, computed via a dynamic consensus approach. Our algorithm enables the agents to use uncoordinated step-sizes. We prove convergence of the local variable of each agent to the optimal solution of the aggregate optimization problem, without requiring decreasing step-sizes. In addition, we demonstrate the efficacy of our algorithm in distributed state estimation problems, and its robust counterparts, where we show its performance compared to existing distributed first-order optimization methods.
Data security and sharing remains nuisance among many applications like business data, medical data, banking data etc. In this research, block chain technology is built with encryption algorithm for high level data security in cloud storage. Medical data security seems critical aspect due to sensitivity of patient information. Unauthorized access of medical data creates major issue to patients. This article proposed block chain with hybrid encryption technique for securing medical data stored in block chain model at cloud storage. New Two fish encryption model is implemented based on RSA Multiple Precision Arithmetic. MPA works by using library concept. The objective of using this methodology is to enhance security performance with less execution time. Patient data is processed by encryption algorithm and stored at blockchain infrastructure using encrypted key. Access permission allows user to read or write the medical data attached in block chain framework. The performance of traditional cryptographic techniques is very less in providing security infrastructure.
Vector graphics are an industry-standard way to represent and share visual designs. Designers frequently source and incorporate styles from existing designs into their own work. Unfortunately, popular design tools aren't well suited for this task. We present VST, Vector Style Transfer, a novel design tool for flexibly transferring visual styles between vector graphics. The core of VST lies in leveraging automation while respecting designers' tastes and the subjectivity inherent to style transfer. In VST, designers tune a cross-design element correspondence and customize which style attributes to change. We report results from a user study in which designers used VST to control style transfer between several designs, including designs participants created with external tools beforehand. VST shows that enabling design correspondence tuning and customization is one way to support interactive, flexible style transfer. We also find that someone using VST can significantly reduce the time and work for style transfer compared to experienced designers using industry-standard tools.
Creating resilient machine learning (ML) systems has become necessary to ensure production-ready ML systems that acquire user confidence seamlessly. The quality of the input data and the model highly influence the successful end-to-end testing in data-sensitive systems. However, the testing approaches of input data are not as systematic and are few compared to model testing. To address this gap, this paper presents the Fault Injection for Undesirable Learning in input Data (FIUL-Data) testing framework that tests the resilience of ML models to multiple intentionally-triggered data faults. Data mutators explore vulnerabilities of ML systems against the effects of different fault injections. The proposed framework is designed based on three main ideas: The mutators are not random; one data mutator is applied at an instance of time, and the selected ML models are optimized beforehand. This paper evaluates the FIUL-Data framework using data from analytical chemistry, comprising retention time measurements of anti-sense oligonucleotide. Empirical evaluation is carried out in a two-step process in which the responses of selected ML models to data mutation are analyzed individually and then compared with each other. The results show that the FIUL-Data framework allows the evaluation of the resilience of ML models. In most experiments cases, ML models show higher resilience at larger training datasets, where gradient boost performed better than support vector regression in smaller training sets. Overall, the mean squared error metric is useful in evaluating the resilience of models due to its higher sensitivity to data mutation.
PyPI provides a convenient and accessible package management platform to developers, enabling them to quickly implement specific functions and improve work efficiency. However, the rapid development of the PyPI ecosystem has led to a severe problem of malicious package propagation. Malicious developers disguise malicious packages as normal, posing a significant security risk to end-users. To this end, we conducted an empirical study to understand the characteristics and current state of the malicious code lifecycle in the PyPI ecosystem. We first built an automated data collection framework and collated a multi-source malicious code dataset containing 4,669 malicious package files. We preliminarily classified these malicious code into five categories based on malicious behaviour characteristics. Our research found that over 50% of malicious code exhibits multiple malicious behaviours, with information stealing and command execution being particularly prevalent. In addition, we observed several novel attack vectors and anti-detection techniques. Our analysis revealed that 74.81% of all malicious packages successfully entered end-user projects through source code installation, thereby increasing security risks. A real-world investigation showed that many reported malicious packages persist in PyPI mirror servers globally, with over 72% remaining for an extended period after being discovered. Finally, we sketched a portrait of the malicious code lifecycle in the PyPI ecosystem, effectively reflecting the characteristics of malicious code at different stages. We also present some suggested mitigations to improve the security of the Python open-source ecosystem.
We provide finite-sample performance guarantees for control policies executed on stochastic robotic systems. Given an open- or closed-loop policy and a finite set of trajectory rollouts under the policy, we bound the expected value, value-at-risk, and conditional-value-at-risk of the trajectory cost, and the probability of failure in a sparse rewards setting. The bounds hold, with user-specified probability, for any policy synthesis technique and can be seen as a post-design safety certification. Generating the bounds only requires sampling simulation rollouts, without assumptions on the distribution or complexity of the underlying stochastic system. We adapt these bounds to also give a constraint satisfaction test to verify safety of the robot system. Furthermore, we extend our method to apply when selecting the best policy from a set of candidates, requiring a multi-hypothesis correction. We show the statistical validity of our bounds in the Ant, Half-cheetah, and Swimmer MuJoCo environments and demonstrate our constraint satisfaction test with the Ant. Finally, using the 20 degree-of-freedom MuJoCo Shadow Hand, we show the necessity of the multi-hypothesis correction.
Unit testing validates the correctness of the unit under test and has become an essential activity in software development process. A unit test consists of a test prefix that drives the unit under test into a particular state, and a test oracle (e.g., assertion), which specifies the behavior in that state. To reduce manual efforts in conducting unit testing, Yu et al. proposed an integrated approach (integration for short), combining information retrieval (IR) with a deep learning-based approach, to generate assertions for a unit test. Despite promising, there is still a knowledge gap as to why or where integration works or does not work. In this paper, we describe an in-depth analysis of the effectiveness of integration. Our analysis shows that: 1) The overall performance of integration is mainly due to its success in retrieving assertions. 2) integration struggles to understand the semantic differences between the retrieved focal-test (focal-test includes a test prefix and a unit under test) and the input focal-test; 3) integration is limited to specific types of edit operations and cannot handle token addition or deletion. To improve the effectiveness of assertion generation, this paper proposes a novel retrieve-and-edit approach named EditAS. Specifically, EditAS first retrieves a similar focal-test from a pre-defined corpus and treats its assertion as a prototype. Then, EditAS reuses the information in the prototype and edits the prototype automatically. EditAS is more generalizable than integration. We conduct experiments on two large-scale datasets and experimental results demonstrate that EditAS outperforms the state-of-the-art approaches, with an average improvement of 10.00%-87.48% and 3.30%-42.65% in accuracy and BLEU score, respectively.
Automatic speech recognition (ASR) systems have been shown to have large quality disparities between the language varieties they are intended or expected to recognize. One way to mitigate this is to train or fine-tune models with more representative datasets. But this approach can be hindered by limited in-domain data for training and evaluation. We propose a new way to improve the robustness of a US English short-form speech recognizer using a small amount of out-of-domain (long-form) African American English (AAE) data. We use CORAAL, YouTube and Mozilla Common Voice to train an audio classifier to approximately output whether an utterance is AAE or some other variety including Mainstream American English (MAE). By combining the classifier output with coarse geographic information, we can select a subset of utterances from a large corpus of untranscribed short-form queries for semi-supervised learning at scale. Fine-tuning on this data results in a 38.5% relative word error rate disparity reduction between AAE and MAE without reducing MAE quality.
Benefit from the quick development of deep learning techniques, salient object detection has achieved remarkable progresses recently. However, there still exists following two major challenges that hinder its application in embedded devices, low resolution output and heavy model weight. To this end, this paper presents an accurate yet compact deep network for efficient salient object detection. More specifically, given a coarse saliency prediction in the deepest layer, we first employ residual learning to learn side-output residual features for saliency refinement, which can be achieved with very limited convolutional parameters while keep accuracy. Secondly, we further propose reverse attention to guide such side-output residual learning in a top-down manner. By erasing the current predicted salient regions from side-output features, the network can eventually explore the missing object parts and details which results in high resolution and accuracy. Experiments on six benchmark datasets demonstrate that the proposed approach compares favorably against state-of-the-art methods, and with advantages in terms of simplicity, efficiency (45 FPS) and model size (81 MB).
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