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A large-scale study of call graph-based impact prediction using mutation testing

Vincenzo Musco, Martin Monperrus, Philippe Preux
2016 Software quality journal  
This evaluation technique enables us to analyze impact prediction based on four types of call graph. Our results show that graph sophistication increases the completeness of impact prediction.  ...  However, and surprisingly to us, the most basic call graph gives the best trade-off between precision and recall for impact prediction.  ...  prediction. • a large scale impact prediction experiment on 10 projects and 17,000 mutants comparing these 4 kinds of call graphs.  ... 
doi:10.1007/s11219-016-9332-8 fatcat:a7rqhdjxjfgejknuof6p67uvxm

A learning algorithm for change impact prediction

Vincenzo Musco, Antonin Carette, Martin Monperrus, Philippe Preux
2016 Proceedings of the 5th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering - RAISE '16  
Change impact analysis consists in predicting the impact of a code change in a software application.  ...  The artifacts that are considered are methods of object-oriented software, the change under study is a change in the code of the method, the impact is the test methods that fail because of the change that  ...  However, we have a different goal, they study test case prioritization, we study impact prediction. Hattori et al. [11] have used an approach based on call graphs to study the propagation.  ... 
doi:10.1145/2896995.2896996 dblp:conf/icse/MuscoCMP16 fatcat:qovrrwjxfrhppcjxw5umalfllu

mCSM: predicting the effects of mutations in proteins using graph-based signatures

Douglas E. V. Pires, David B. Ascher, Tom L. Blundell
2013 Computer applications in the biosciences : CABIOS  
Here, we propose a novel approach to the study of missense mutations, called mCSM, which relies on graph-based signatures.  ...  The mCSM signatures were successfully used in different tasks demonstrating that the impact of a mutation can be correlated with the atomic-distance patterns surrounding an amino acid residue.  ...  Here, we use the concept of graph-based structural signatures to study and predict the impact of single-point mutations on protein stability and protein-protein and protein-nucleic acid affinity.  ... 
doi:10.1093/bioinformatics/btt691 pmid:24281696 pmcid:PMC3904523 fatcat:nhssxusikfeipov3sthj5tv6tm

The central role of test automation in software quality assurance

Leonardo Mariani, Dan Hao, Rajesh Subramanyan, Hong Zhu
2017 Software quality journal  
In the past few years, the workshops have attracted large numbers of high-quality papers and large numbers of delegates participating in the active exchanges of research results and practical experiences  ...  Welcome to this special section on the Automation of Software Test.  ...  In the paper entitled A Large Scale Study of Call Graph-based Impact Prediction using Mutation Testing, Vincenzo Musco, Martin Monperrus, and Philippe Preux propose a method for evaluating impact propagation  ... 
doi:10.1007/s11219-017-9383-5 fatcat:htiuaa2xtna7vprekogfxqcxru

Proteome-wide search for functional motifs altered in tumors: Prediction of nuclear export signals inactivated by cancer-related mutations

Gorka Prieto, Asier Fullaondo, Jose A. Rodríguez
2016 Scientific Reports  
In silico prediction of functionally relevant amino acid motifs disrupted by cancer mutations could provide insight into the potential impact of a mutation, and guide functional tests.  ...  Large-scale sequencing projects are uncovering a growing number of missense mutations in human tumors.  ...  This work was supported by the Spanish Ministry of Economy (grant SAF2014-57743-R to JAR), and by the University of the Basque Country (UFI 11/20).  ... 
doi:10.1038/srep25869 pmid:27174732 pmcid:PMC4865848 fatcat:kiff6go2qvhq7pymtifdsbw5yi

MuDelta: Delta-Oriented Mutation Testing at Commit Time

Wei Ma, Thierry Titcheu Chekam, Mike Papadakis, Mark Harman
2021 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)  
Our approach uses machine learning applied on a combined scheme of graph and vector-based representations of static code features.  ...  To effectively test program changes using mutation testing, one needs to use mutants that are relevant to the altered program behaviours.  ...  We use a large number of features, selected either based on previous studies [20] or by using our intuition, which are automatically filtered by gradient boosting.  ... 
doi:10.1109/icse43902.2021.00086 fatcat:cz5c4jans5gdfmopaew33kbnq4

Pre-training of Graph Neural Network for Modeling Effects of Mutations on Protein-Protein Binding Affinity [article]

Xianggen Liu, Yunan Luo, Sen Song, Jian Peng
2020 arXiv   pre-print
In this study, we develop a novel deep learning based framework, named GraphPPI, to predict the binding affinity changes upon mutations based on the features provided by a graph neural network (GNN).  ...  These results have established GraphPPI as a powerful and useful computational tool in the studies of protein design.  ...  Before testing, Graph-PPI was trained on all the data points of the large-scale M1707 dataset.  ... 
arXiv:2008.12473v1 fatcat:jvfoslqvorgw3hjvbo7epsa47u

Prediction by Graph Theoretic Measures of Structural Effects in Proteins Arising from Non-Synonymous Single Nucleotide Polymorphisms

Tammy M. K. Cheng, Yu-En Lu, Michele Vendruscolo, Pietro Lio', Tom L. Blundell, Ruth Nussinov
2008 PLoS Computational Biology  
In order to explore the potential of such information, we developed a structure-based approach, Bongo (Bonds ON Graph), to predict structural effects of nsSNPs.  ...  Application of the Bongo method to the prediction of 506 disease-associated nsSNPs resulted in a performance (positive predictive value, PPV, 78.5%) similar to that of PolyPhen (PPV, 77.2%) and PANTHER  ...  Conclusions We have developed a method, Bongo, which uses graph theoretic measures to evaluate the structural impacts of single point mutations.  ... 
doi:10.1371/journal.pcbi.1000135 pmid:18654622 pmcid:PMC2447880 fatcat:nrawwhgvorggxdohix34vjhloy

Predicting links between tumor samples and genes using 2-Layered graph based diffusion approach

Mohan Timilsina, Haixuan Yang, Ratnesh Sahay, Dietrich Rebholz-Schuhmann
2019 BMC Bioinformatics  
Results: Here we present, a computational model based on a heat diffusion algorithm which can predict the association between tumor samples and genes. We proposed a 2-layered graph.  ...  In the first layer, we constructed a graph of tumor samples and genes where these two types of nodes are connected by "hasGene" relationship.  ...  Analytics, National University of Ireland Galway, Galway, Ireland. 2 School of Mathematics Statistics and Applied Mathematics, National University of Ireland Galway, Galway, Ireland.  ... 
doi:10.1186/s12859-019-3056-2 fatcat:qk7blqhucvf3pi3z4wjrlilh2e

Mutation-Based Graph Inference for Fault Localization

Vincenzo Musco, Martin Monperrus, Philippe Preux
2016 2016 IEEE 16th International Working Conference on Source Code Analysis and Manipulation (SCAM)  
We present a new fault localization algorithm, called Vautrin, built on an approximation of causality based on call graphs. The approximation of causality is done using software mutants.  ...  The key idea is that if a mutant is killed by a test, certain call graph edges within a path between the mutation point and the failing test are likely causal.  ...  First, it is required so that the approach scales to large software (up to thousands of nodes and edges as shown later in the evaluation).  ... 
doi:10.1109/scam.2016.24 dblp:conf/scam/MuscoMP16 fatcat:xmdygv3psbee7dp2vajecsxneu

A fundamental protein property, thermodynamic stability, revealed solely from large-scale measurements of protein function

C. L. Araya, D. M. Fowler, W. Chen, I. Muniez, J. W. Kelly, S. Fields
2012 Proceedings of the National Academy of Sciences of the United States of America  
Based on this observation, we introduce partner potentiation, a metric that uses this rescue ability to identify stabilizing mutations, and identify 15 candidate stabilizing mutations in the WW domain.  ...  Here, we measure the ability of 47,000 variants of a WW domain to bind to a peptide ligand and use these functional measurements to identify stabilizing mutations without directly assaying stability.  ...  S.F. is an investigator of The Howard Hughes Medical Institute. This work was supported by National Institutes of Health Grants F32GM084699 (to D.M.F), GM051105 (to J.W.K.), and P41GM103533 (to S.F.).  ... 
doi:10.1073/pnas.1209751109 pmid:23035249 pmcid:PMC3479514 fatcat:d2akwmubgjhlpg66nejbmgbvi4

Proteogenomic strategies for identification of aberrant cancer peptides using large-scale next-generation sequencing data

Sunghee Woo, Seong Won Cha, Seungjin Na, Clark Guest, Tao Liu, Richard D. Smith, Karin D. Rodland, Samuel Payne, Vineet Bafna
2014 Proteomics  
This paper provides a discussion of applying different strategies relating to large database search, and FDR(False Discovery Rate) based error control, and their implication to cancer proteogenomics.  ...  Combination of proteomic and genomic technologies are increasingly being employed. Various strategies have been employed to allow the usage of large scale NGS data for conventional MS/MS searches.  ...  In comparison, our method implements an approach that merges and compress large-scale RNA-seq data (all) into a single database by applying graph-based algorithm.  ... 
doi:10.1002/pmic.201400206 pmid:25263569 pmcid:PMC4256132 fatcat:aq5sdheenbcyvll3osbx4rfgse

TAIC PART 2007 and Mutation 2007 special issue editorial

Mark Harman, Zheng Li, Phil McMinn, Jeff Offutt, John Clark
2009 Journal of Systems and Software  
The empirical evaluation includes a large-scale industrial embedded system.  ...  The second, a paper by Ben H. Smith and Laurie Williams, presents an empirical study of the idea of using mutation analysis to improve an existing test set.  ... 
doi:10.1016/j.jss.2009.06.028 fatcat:bejjjxyg7fbvzi6fzh3ze6zk54

Rapid antibiotic resistance predictions from genome sequence data for S. aureus and M. tuberculosis [article]

Phelim Bradley, N Claire Gordon, Timothy M Walker, Laura Dunn, Simon Heys, Bill Huang, Sarah Earle, Louise J Pankhurst, Luke Anson, Mariateresa de Cesare, Paolo Piazza, Antonina A Votintseva (+16 others)
2015 bioRxiv   pre-print
Here, we address these challenges, using a de Bruijn graph comparison of clinical isolate and curated knowledge-base to identify species and predict resistance profile, including minor populations.  ...  Rapid and accurate detection of antibiotic resistance in pathogens is an urgent need, affecting both patient care and population-scale control.  ...  This issue, shared by all molecular assays, can only be resolved by large-scale sequencing and phenotyping studies.  ... 
doi:10.1101/018564 fatcat:5bfwvv6tknhqdnggbf7peqwwsm

Deciphering oncogenic drivers: from single genes to integrated pathways

J. Chen, M. Sun, B. Shen
2014 Briefings in Bioinformatics  
These approaches will help reduce the mutation complexity, thus providing a simplified picture of cancer.  ...  Technological advances in next-generation sequencing have uncovered a wide spectrum of aberrations in cancer genomes.  ...  Mutation Assessor [26] provides a direct prediction of the functional impact of a mutation, also based on the multiple sequence alignments and evolutionary analysis.  ... 
doi:10.1093/bib/bbu039 pmid:25378434 fatcat:njjzzvuganalphm526kvmrrwpu
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