134,804 research outputs found
Learning to Combine Multiple Ranking Metrics for Fault Localization
International audienceFault localization is an inevitable step in software debugging. Spectrum-based fault localization consists in computing a ranking metric on execution traces to identify faulty source code. Existing empirical studies on fault localization show that there is no optimal ranking metric for all faults in practice. In this paper, we propose Multric, a learning-based approach to combining multiple ranking metrics for effective fault localization. In Multric, a suspiciousness score of a program entity is a combination of existing ranking metrics. Multric consists two major phases: learning and ranking. Based on training faults, Multric builds a ranking model by learning from pairs of faulty and non-faulty source code elements. When a new fault appears, Multric computes the final ranking with the learned model. Experiments are conducted on 5386 seeded faults in ten open-source Java programs. We empirically compare Multric against four widely-studied metrics and three recently-proposed one. Our experimental results show that Multric localizes faults more effectively than state-of-art metrics, such as Tarantula, Ochiai, and Ample
FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile Devices
Indoor localization plays a vital role in applications such as emergency
response, warehouse management, and augmented reality experiences. By deploying
machine learning (ML) based indoor localization frameworks on their mobile
devices, users can localize themselves in a variety of indoor and subterranean
environments. However, achieving accurate indoor localization can be
challenging due to heterogeneity in the hardware and software stacks of mobile
devices, which can result in inconsistent and inaccurate location estimates.
Traditional ML models also heavily rely on initial training data, making them
vulnerable to degradation in performance with dynamic changes across indoor
environments. To address the challenges due to device heterogeneity and lack of
adaptivity, we propose a novel embedded ML framework called FedHIL. Our
framework combines indoor localization and federated learning (FL) to improve
indoor localization accuracy in device-heterogeneous environments while also
preserving user data privacy. FedHIL integrates a domain-specific selective
weight adjustment approach to preserve the ML model's performance for indoor
localization during FL, even in the presence of extremely noisy data.
Experimental evaluations in diverse real-world indoor environments and with
heterogeneous mobile devices show that FedHIL outperforms state-of-the-art FL
and non-FL indoor localization frameworks. FedHIL is able to achieve 1.62x
better localization accuracy on average than the best performing FL-based
indoor localization framework from prior work
RLocator: Reinforcement Learning for Bug Localization
Software developers spend a significant portion of time fixing bugs in their
projects. To streamline this process, bug localization approaches have been
proposed to identify the source code files that are likely responsible for a
particular bug. Prior work proposed several similarity-based machine-learning
techniques for bug localization. Despite significant advances in these
techniques, they do not directly optimize the evaluation measures. We argue
that directly optimizing evaluation measures can positively contribute to the
performance of bug localization approaches. Therefore, In this paper, we
utilize Reinforcement Learning (RL) techniques to directly optimize the ranking
metrics. We propose RLocator, a Reinforcement Learning-based bug localization
approach. We formulate RLocator using a Markov Decision Process (MDP) to
optimize the evaluation measures directly. We present the technique and
experimentally evaluate it based on a benchmark dataset of 8,316 bug reports
from six highly popular Apache projects. The results of our evaluation reveal
that RLocator achieves a Mean Reciprocal Rank (MRR) of 0.62, a Mean Average
Precision (MAP) of 0.59, and a Top 1 score of 0.46. We compare RLocator with
two state-of-the-art bug localization tools, FLIM and BugLocator. Our
evaluation reveals that RLocator outperforms both approaches by a substantial
margin, with improvements of 38.3% in MAP, 36.73% in MRR, and 23.68% in the Top
K metric. These findings highlight that directly optimizing evaluation measures
considerably contributes to performance improvement of the bug localization
problem
Repairing Deep Neural Networks: Fix Patterns and Challenges
Significant interest in applying Deep Neural Network (DNN) has fueled the
need to support engineering of software that uses DNNs. Repairing software that
uses DNNs is one such unmistakable SE need where automated tools could be
beneficial; however, we do not fully understand challenges to repairing and
patterns that are utilized when manually repairing DNNs. What challenges should
automated repair tools address? What are the repair patterns whose automation
could help developers? Which repair patterns should be assigned a higher
priority for building automated bug repair tools? This work presents a
comprehensive study of bug fix patterns to address these questions. We have
studied 415 repairs from Stack overflow and 555 repairs from Github for five
popular deep learning libraries Caffe, Keras, Tensorflow, Theano, and Torch to
understand challenges in repairs and bug repair patterns. Our key findings
reveal that DNN bug fix patterns are distinctive compared to traditional bug
fix patterns; the most common bug fix patterns are fixing data dimension and
neural network connectivity; DNN bug fixes have the potential to introduce
adversarial vulnerabilities; DNN bug fixes frequently introduce new bugs; and
DNN bug localization, reuse of trained model, and coping with frequent releases
are major challenges faced by developers when fixing bugs. We also contribute a
benchmark of 667 DNN (bug, repair) instances
Indoor positioning with deep learning for mobile IoT systems
2022 Summer.Includes bibliographical references.The development of human-centric services with mobile devices in the era of the Internet of Things (IoT) has opened the possibility of merging indoor positioning technologies with various mobile applications to deliver stable and responsive indoor navigation and localization functionalities that can enhance user experience within increasingly complex indoor environments. But as GPS signals cannot easily penetrate modern building structures, it is challenging to build reliable indoor positioning systems (IPS). Currently, Wi-Fi sensing based indoor localization techniques are gaining in popularity as a means to build accurate IPS, benefiting from the prevalence of 802.11 family. Wi-Fi fingerprinting based indoor localization has shown remarkable performance over geometric mapping in complex indoor environments by taking advantage of pattern matching techniques. Today, the two main information extracted from Wi-Fi signals to form fingerprints are Received Signal Strength Index (RSSI) and Channel State Information (CSI) with Orthogonal Frequency-Division Multiplexing (OFDM) modulation, where the former can provide the average localization error around or under 10 meters but has low hardware and software requirements, while the latter has a higher chance to estimate locations with ultra-low distance errors but demands more resources from chipsets, firmware/software environments, etc. This thesis makes two novel contributions towards realizing viable IPS on mobile devices using RSSI and CSI information, and deep machine learning based fingerprinting. Due to the larger quantity of data and more sophisticated signal patterns to create fingerprints in complex indoor environments, conventional machine learning algorithms that need carefully engineered features suffer from the challenges of identifying features from very high dimensional data. Hence, the abilities of approximation functions generated from conventional machine learning models to estimate locations are limited. Deep machine learning based approaches can overcome these challenges to realize scalable feature pattern matching approaches such as fingerprinting. However, deep machine learning models generally require considerable memory footprint, and this creates a significant issue on resource-constrained devices such as mobile IoT devices, wearables, smartphones, etc. Developing efficient deep learning models is a critical factor to lower energy consumption for resource intensive mobile IoT devices and accelerate inference time. To address this issue, our first contribution proposes the CHISEL framework, which is a Wi-Fi RSSI- based IPS that incorporates data augmentation and compression-aware two-dimensional convolutional neural networks (2D CAECNNs) with different pruning and quantization options. The proposed model compression techniques help reduce model deployment overheads in the IPS. Unlike RSSI, CSI takes advantages of multipath signals to potentially help indoor localization algorithms achieve a higher level of localization accuracy. The compensations for magnitude attenuation and phase shifting during wireless propagation generate different patterns that can be utilized to define the uniqueness of different locations of signal reception. However, all prior work in this domain constrains the experimental space to relatively small-sized and rectangular rooms where the complexity of building interiors and dynamic noise from human activities, etc., are seldom considered. As part of our second contribution, we propose an end-to-end deep learning based framework called CSILoc for Wi-Fi CSI-based IPS on mobile IoT devices. The framework includes CSI data collection, clustering, denoising, calibration and classification, and is the first study to verify the feasibility to use CSI for floor level indoor localization with minimal knowledge of Wi-Fi access points (APs), thus avoiding security concerns during the offline data collection process
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