Effective Software Fault Localization Using an RBF Neural Network

Wong, W. Eric; Debroy, Vidroha; Golden, Richard M.; Xu, Xiaofeng; Thuraisingham, Bhavani

doi:10.1109/tr.2011.2172031

Cited by 159 publications

(65 citation statements)

References 44 publications

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“…The proposed protocol uses the radial basis function (RBF) [42] to create the virtual co-operative neighborhood regions across the network. SDN provide the flexibility of virtualizing the network planes.…”

Section: H Co-operative Radial-basis Neighborhood System For Distribmentioning

confidence: 99%

Software Defined Network: A Survey

2017

IJAERD

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Section: H Co-operative Radial-basis Neighborhood System For Distribmentioning

confidence: 99%

Software Defined Network: A Survey

2017

IJAERD

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“…Furthermore, the importance of logging is also recognized by developers: a recent survey reported that 96% of a group of experienced developers from a leading software company "strongly agree/agree that logging statements are important in system development and maintenance" [13]. Indeed, the information contained in log files can be used for a variety of purposes, such as process mining [15,30], anomaly detection [4,12,14], behavioral differencing [14], fault localization [33], invariant inference [5], performance diagnosis [21], and offline trace checking [3].…”

Section: Introductionmentioning

confidence: 99%

A search-based approach for accurate identification of log message formats

Messaoudi

Panichella

Bianculli

et al. 2018

Proceedings of the 26th Conference on Program Comprehension

116

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Many software engineering activities process the events contained in log files. However, before performing any processing activity, it is necessary to parse the entries in a log file, to retrieve the actual events recorded in the log. Each event is denoted by a log message, which is composed of a fixed part-called (event) template-that is the same for all occurrences of the same event type, and a variable part, which may vary with each event occurrence. The formats of log messages, in complex and evolving systems, have numerous variations, are typically not entirely known, and change on a frequent basis; therefore, they need to be identified automatically.The log message format identification problem deals with the identification of the different templates used in the messages of a log. Any solution to this problem has to generate templates that meet two main goals: generating templates that are not too general, so as to distinguish different events, but also not too specific, so as not to consider different occurrences of the same event as following different templates; however, these goals are conflicting.In this paper, we present the MoLFI approach, which recasts the log message identification problem as a multi-objective problem. MoLFI uses an evolutionary approach to solve this problem, by tailoring the NSGA-II algorithm to search the space of solutions for a Pareto optimal set of message templates. We have implemented MoLFI in a tool, which we have evaluated on six real-world datasets, containing log files with a number of entries ranging from 2K to 300K. The experiments results show that MoLFI extracts by far the highest number of correct log message templates, significantly outperforming two state-of-the-art approaches on all datasets. CCS CONCEPTS• Software and its engineering → Search-based software engineering;

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“…We notice that machine learning techniques were successfully applied in the field of fault localization [4], [5]. However, many models have drawbacks such as restricted generalization ability for intricate problem and inadequate ability in using limited data set to express complicated functions.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Fault Localization with Contextual Information

Zhang

Lei

Tan

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYFault localization is essential for solving the issue of software faults. Aiming at improving fault localization, this paper proposes a deep learning-based fault localization with contextual information. Specifically, our approach uses deep neural network to construct a suspiciousness evaluation model to evaluate the suspiciousness of a statement being faulty, and then leverages dynamic backward slicing to extract contextual information. The empirical results show that our approach significantly outperforms the state-of-the-art technique Dstar.

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Effective Software Fault Localization Using an RBF Neural Network

Cited by 159 publications

References 44 publications

Software Defined Network: A Survey

Software Defined Network: A Survey

A search-based approach for accurate identification of log message formats

Deep Learning-Based Fault Localization with Contextual Information

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