In software development, debugging is the most tedious and time-consuming phase. Therefore, various automated fault localization techniques have been proposed to assist debugging. Among existing fault localization tec...
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In software development, debugging is the most tedious and time-consuming phase. Therefore, various automated fault localization techniques have been proposed to assist debugging. Among existing fault localization techniques, Spectrum-Based Fault Localization (SBFL) is one of the most extensively researched methods. Traditional SBFL techniques rely solely on the coverage of program execution for fault localization, which means they neglect the interactions between program entities and fault propagation paths during the execution of the program, resulting in a tie problem that reduces the accuracy of fault localization. To solve the above problem, this paper proposes SA-SBFL, a fault localization method based on the salsa (Random Method for Link Structure Analysis) algorithm. First, a link graph of program entities is constructed, which includes interactions between program entities and fault propagation paths. Then, the suspicion values obtained from traditional SBFL methods are used as the initial weights of the link graph. Finally, the random walk model is employed to simulate the propagation of faults among program entities, analyze the importance of program entities in the fault propagation process, and obtain a ranking list of suspicious program entities. The experiments in this paper demonstrate that the SA-SBFL method significantly outperforms general SBFL methods. For instance, in the Defects4J dataset, the SA-SBFL technique outperforms traditional SBFL in terms of fault localization accuracy, with a 47% improvement in the Top-1 metric and a 10% increase in the Top-5 metric, and it also showed an average improvement of 19% in the EXAM metric.
In response to the traditional Dempster-Shafer (D-S) combination rule that cannot handle highly conflicting evidence, an evidence combination method based on the stochastic approach for link-structure analysis (salsa)...
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In response to the traditional Dempster-Shafer (D-S) combination rule that cannot handle highly conflicting evidence, an evidence combination method based on the stochastic approach for link-structure analysis (salsa) algorithm combined with Lance-Williams distance is proposed. Firstly, the degree of conflict between evidences is calculated based on the number of correlation coefficients between evidences. Then, the evidences with a number of correlation coefficients greater than the average number of correlation coefficients of evidence are connected to construct an evidence association network. The authority weight of the evidence is calculated based on the number of citations in the concept of salsa algorithm combined with the support of the evidence. Subsequently, the Lance-Williams distance between the evidences is calculated and transformed into support of the evidence. Next, the authority weight and support of evidence are combined to jointly construct a novel correction coefficient to correct the evidence. Finally, the corrected evidence is fused using the D-S combination rule to obtain the final fusion result. The numerical results verify that the method proposed in this paper can effectively solve the problem of the traditional D-S combination rule being unable to handle highly conflicting evidence.
algorithms such as Kleinberg's HITS algorithm, the PageRank algorithm of Brin and Page, and the salsa algorithm of Lempel and Moran use the link structure of a network of web pages to assign weights to each page i...
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algorithms such as Kleinberg's HITS algorithm, the PageRank algorithm of Brin and Page, and the salsa algorithm of Lempel and Moran use the link structure of a network of web pages to assign weights to each page in the network. The weights can then be used to rank the pages as authoritative sources. These algorithms share a common underpinning;they find a dominant eigenvector of a nonnegative matrix that describes the link structure of the given network and use the entries of this eigenvector as the page weights. We use this commonality to give a unified treatment, proving the existence of the required eigenvector for the PageRank, HITS, and salsa algorithms, the uniqueness of the PageRank eigenvector, and the convergence of the algorithms to these eigenvectors. However, we show that the HITS and salsa eigenvectors need not be unique. We examine how the initialization of the algorithms affects the final weightings produced. We give examples of networks that lead the HITS and salsa algorithms to return nonunique or nonintuitive rankings. We characterize all such networks in terms of the connectivity of the related HITS authority graph. We propose a modi. cation, Exponentiated Input to HITS, to the adjacency matrix input to the HITS algorithm. We prove that Exponentiated Input to HITS returns a unique ranking, provided that the network is weakly connected. Our examples also show that salsa can give inconsistent hub and authority weights, due to nonuniqueness. We also mention a small modi. cation to the salsa initialization which makes the hub and authority weights consistent.
The reactions of two model mutagenic and carcinogenic alkylating agents, N-methyl-N-nitrosourea (MINU) and methyl methanesulfonate (MMS), with proteins and deoxynucleosides in vitro, were investigated. The protein wor...
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The reactions of two model mutagenic and carcinogenic alkylating agents, N-methyl-N-nitrosourea (MINU) and methyl methanesulfonate (MMS), with proteins and deoxynucleosides in vitro, were investigated. The protein work used an approach involving trypsin digestion and high-performance liquid chromatography/electrospray ionization tandem mass spectrometry (HPLC/ESI-MS/MS). This technique permitted identification of the specific location of protein adduction by both MNU and MMS with commercial apomyoglobin and human hemoglobin, under physiological conditions. MNU treatment resulted in predominantly carbamoylation adducts on the proteins, but in contrast only methylated protein adducts were found following treatment with MMS. Further analyses, using TurboSequest(R), and the Scoring algorithm for Spectral Analysis (salsa), revealed that MNU carbamoylation was specific for modification of either the N-terminal valine or the free amino group in lysine residues of apomyglobin and human hemoglobin. However, MMS methylation modified the N-terminal valine and histidine residues of the proteins. Despite their clear differences in protein modifications, MNU and MMS formed qualitatively the same methylated deoxynucleoside adduct profiles with all four deoxynucleosides in vitro under physiological conditions. In light of their different biological potencies, where MMS is considered a 'super clastogen' while MNU is a 'super mutagen', these differences in reaction products with proteins vs. deoxynucleosides may indicate that these two model alkylating agents work via different mechanisms to produce their mutagenic and carcinogenic effects. Copyright (C) 2005 John Wiley Sons, Ltd.
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