Effective Software Fault Localization Using a Back Propagation Neural Network

Maru, Abha; Dutta, Arpita; Kumar, Kulamala Vinod; Mohapatra, Durga Prasad

doi:10.1007/978-981-13-8676-3_44

Cited by 9 publications

(3 citation statements)

References 24 publications

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“…Figure 2 have much effect on increasing network accuracy. Increasing the number of neural cells in the middle layer increases the accuracy of the neural network, but excessively increasing the number of neural cells decreases the accuracy of the network because any increase will not always improve the network [26]. MATLAB has been used to implement DL.…”

Section: The Study Area and Resultsmentioning

confidence: 99%

Deep learning of total electron content

Sorkhabi

2021

SN Appl. Sci.

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One of the most notable errors in the global navigation satellite system (GNSS) is the ionospheric delay due to the total electron content (TEC). TEC is the number of electrons in the ionosphere in the signal path from the satellite to the receiver, which fluctuates with time and location. This error is one of the major problems in single-frequency (SF) GPS receivers. One way to eliminate this error is to use dual-frequency. Users of SF receivers should either use estimation models or local models to reduce this error. In this study, deep learning of artificial neural networks (ANN) was used to estimate TEC for SF users. For this purpose, the ionosphere as a single-layer model (assuming that all free electrons in the ionosphere are in this thin layer) is locally modeled by the code observation method. Linear combination has been used by selecting 24 permanent GNSS stations in the northwest of Iran. TEC was modeled independently of the geometry between the satellite and the receiver, called L4. This modeling was used to train the error ANN with two 5-day periods of high and low solar and geomagnetic activity range with a hyperbolic tangential sigmoid activation function. The results show that the proposed method is capable of eliminating ionosphere error with an average accuracy of 90%. The international reference ionosphere 2016 (IRI2016) is used for the verification, which has a 96% significance correlation with estimated TEC.

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Section: The Study Area and Resultsmentioning

confidence: 99%

Deep learning of total electron content

Sorkhabi

2021

SN Appl. Sci.

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“…Maru et al [25] utilized a back propagation neural network in which the actual number of times the statement is executed to train the network and got a 35% increase in the effectiveness over existing BPNN. Zakari et al [26] reviewed existing research on Multiple fault localization (MFL) in software fault localization.…”

Section: Literature Reviewmentioning

confidence: 99%

A Learning-Based Fault Localization Approach Using Subset of Likely and Dynamic Invariants

Shaikh¹,

Rizwan²,

Alghamdi³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Fault localization is one of the main tasks of software debugging. Developers spend a lot of time, cost, and effort to locate the faults correctly manually. For reducing this effort, many automatic fault localization techniques have been proposed, which inputs test suites and outputs a sorted list of faulty entities of the program. For further enhancement in this area, we developed a system called SILearning, which is based on invariant analysis. It learns from some existing fixed bugs to locate faulty methods in the program. It combines machine-learned ranking, program invariant differences, and spectrum-based fault localization (SBFL). Using the execution of test cases and code coverage analysis, it obtains each method's invariant differences and the suspiciousness value based on the program spectral location and uses them as features for ranking the faulty methods. The experimental analysis of SILearning has been performed on the dataset of real fault which is extracted from the database Defects4J. The tools used in this research are Daikon and cobertura for detection of the invariants and code coverage, respectively. The results show that SILearning performs better when combined features are utilized and can successfully locate the faulty methods on average for 76.1, 90.4, 108.2, 123, and 143.5 at the top positions of 1, 2, 3, 4, and 5.

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“…One possible solution to more accurate pose estimation is introducing deep learning models to RANSAC. But the arg max selection function is non‐differentiable, which means the gradient of objective function cannot be back‐propagated in the network [44] during training. So, softmax function is utilized to make the hypothesis selection differentiable.…”

Section: Log‐slam Systemmentioning

confidence: 99%

Neural guided visual slam system with Laplacian of Gaussian operator

Zhang

Yan

2021

IET Computer Vision

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Simultaneous localization and mapping (SLAM) addresses the problem of constructing the map from noisy sensor data and tracking the robot's path within the built map. After decades of development, a lot of mature systems achieve competent results in feature-based implementations. However, there are still problems when migrating the technology to practical applications. One typical example is the accuracy and robustness of SLAM in environment with illuminance and texture variations. To this end, two modules in the existing systems are improved here namely tracking and camera relocalization. In tracking module, image pyramid is processed with Laplacian of Gaussian (LoG) operator in feature extraction for enhanced edges and details. A majority voting mechanism is proposed to dynamically evaluate and redetermine the zero-mean sum of square difference threshold according to the matching error estimation in patch search. In camera relocalization module, full convolutional neural network which focuses on certain parts of the input data is utilized in guiding for accurate output predictions. The authors implement the two modules into OpenvSLAM and propose a neural guided visual SLAM system named LoG-SLAM. Experiments on publicly available datasets show that the accuracy and efficiency increase with LoG-SLAM when compared with other feature-based methods, and relocalization accuracy also improves compared with the recently proposed deep learning pipelines. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Effective Software Fault Localization Using a Back Propagation Neural Network

Cited by 9 publications

References 24 publications

Deep learning of total electron content

Deep learning of total electron content

A Learning-Based Fault Localization Approach Using Subset of Likely and Dynamic Invariants

Neural guided visual slam system with Laplacian of Gaussian operator

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