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Practical Extensions of a Randomized Testing Tool

Hojun Jaygarl, Carl K. Chang, Sunghun Kim
2009 2009 33rd Annual IEEE International Computer Software and Applications Conference  
Many efficient random testing algorithms for object-oriented software have been proposed due to their simplicity and reasonable code coverage; however, even the stateof-the-art random test algorithms yield  ...  We propose four testing techniques to improve test coverage. The proposed techniques are pluggable to any existing random testing techniques for object-oriented software.  ...  ACKNOWLEDGMENT We thank Carlos Pacheco for providing RANDOOP's source code and test coverage checking code, and supporting.  ... 
doi:10.1109/compsac.2009.29 dblp:conf/compsac/JaygarlCK09 fatcat:i2xb736txjfhpfzufpdxmii4ui

Fragment Analysis and Test Case Generation using F-Measure for Adaptive Random Testing and Partitioned Block based Adaptive Random Testing

D. Indhumathi, S. Sarala
2014 International Journal of Computer Applications  
There exist three measures for evaluating the effectiveness of a testing technique namely P-measure, E-measure and Fmeasure.  ...  Test case generation is a path to identify the solution in software testing. Adaptive random testing is an enhancement of random testing to improve the quality of fault-revealing.  ...  The main merits of random testing [3] include the accessibility of efficient algorithms to generate test cases and infer the reliability with statistical measures.  ... 
doi:10.5120/16218-5662 fatcat:3v2bkcpdtzbizjjtwsbqlecfdm

Evaluating the Effectiveness of BEN in Localizing Different Types of Software Fault

Jaganmohan Chandrasekaran, Laleh Sh. Ghandehari, Yu Lei, Raghu Kacker, D. Richard Kuhn
2016 2016 IEEE Ninth International Conference on Software Testing, Verification and Validation Workshops (ICSTW)  
For tcas program from the Siemens suite, there are eight faulty versions for which, the random test set on execution does not generate a single fail test.  ...  GZIP has complex constraints; it prevented us from generating 1000 unique random test cases. The maximum unique random test cases we were able to generate were 395.  ... 
doi:10.1109/icstw.2016.44 dblp:conf/icst/ChandrasekaranG16 fatcat:em4wxymk6rgirj4a6i64mdx2wy

Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning [article]

Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, Dawn Song
2017 arXiv   pre-print
For example, deep learning-based face recognition systems have been used to authenticate users to access many security-sensitive applications like payment apps.  ...  We conduct evaluation to demonstrate that a backdoor adversary can inject only around 50 poisoning samples, while achieving an attack success rate of above 90%.  ...  ACKNOWLEDGMENT We thank Richard Shin, Warren He, Xiaojun Xu for their help in experiments of physical attacks.  ... 
arXiv:1712.05526v1 fatcat:ebavdwn4evbvvmrudknv7sljeq

Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models [article]

Tong Niu, Mohit Bansal
2018 arXiv   pre-print
We next perform adversarial training with each strategy, employing a max-margin approach for negative generative examples.  ...  We present two categories of model-agnostic adversarial strategies that reveal the weaknesses of several generative, task-oriented dialogue models: Should-Not-Change strategies that evaluate over-sensitivity  ...  Acknowledgments We thank the anonymous reviewers for their helpful comments and discussions.  ... 
arXiv:1809.02079v1 fatcat:2biosb2lpjdx5nt5bxjkv4its4

IReEn: Reverse-Engineering of Black-Box Functions via Iterative Neural Program Synthesis [article]

Hossein Hajipour, Mateusz Malinowski, Mario Fritz
2021 arXiv   pre-print
In contrast to prior work, we do not rely on privileged information on the black box, but rather investigate the problem under a weaker assumption of having only access to inputs and outputs of the program  ...  We approach this problem by iteratively refining a candidate set using a generative neural program synthesis approach until we arrive at a functionally equivalent program.  ...  To evaluate our approach we query each black-box program in the test set with 50 valid inputs to get the corresponding outputs.  ... 
arXiv:2006.10720v2 fatcat:6ey5ta7axbf4lmfwop4xpee4q4

Automated directed fairness testing

Sakshi Udeshi, Pryanshu Arora, Sudipta Chattopadhyay
2018 Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering - ASE 2018  
In our evaluation, AEQUITAS generates up to 70% discriminatory inputs (w.r.t. the total number of inputs generated) and leverages these inputs to improve the fairness up to 94%.  ...  We show that AEQUITAS effectively generates inputs to uncover fairness violation in all the subject classifiers and systematically improves the fairness of the respective models using the generated test  ...  for state-of-the-art models. • Training data and access to model: AEQUITAS needs access to the training data and the training mechanism of the machine-learning model to be able to evaluate and retrain  ... 
doi:10.1145/3238147.3238165 dblp:conf/kbse/UdeshiAC18 fatcat:ev7aktw2zza6rjkkc6tq3lar4y


2016 Biocomputing 2017  
Comparative imputation strategies We used the FancyImpute 21 Missing Completely at Random Imputation Evaluation To evaluate imputation accuracy in a missing completely at random environment we performed  ...  ALS and the Pooled Resource Open-access Clinical Trials We evaluate each of the imputation methods on the ALS Pooled Resource Open-access Clinical Trials (PRO-ACT).  ... 
doi:10.1142/9789813207813_0021 pmid:27896976 pmcid:PMC5144587 fatcat:uz7mkk2e5nh5djjwufjvoennta

GraCIAS: Grassmannian of Corrupted Images for Adversarial Security [article]

Ankita Shukla, Pavan Turaga, Saket Anand
2020 arXiv   pre-print
for the attacker.  ...  In this work, we propose a defense strategy that applies random image corruptions to the input image alone, constructs a self-correlation based subspace followed by a projection operation to suppress the  ...  Our proposed approach, Grassmannian of Corrupted Images for Adversarial Security (GraCIAS) applies a random number of randomized filtering opera-tions to the input test image.  ... 
arXiv:2005.02936v2 fatcat:2ofcn2r23bavtcin2xsxkzphey

Industrial application of concolic testing approach: A case study on libexif by using CREST-BV and KLEE

Yunho Kim, Moonzoo Kim, Young Joo Kim, Yoonkyu Jang
2012 2012 34th International Conference on Software Engineering (ICSE)  
We detected a memory access bug, a null pointer dereference bug, and four divide-by-zero bugs in libexif through concolic testing, none of which were detected by Coverity Prevent.  ...  We also compare two concolic testing tools, CREST-BV and KLEE, in this testing project.  ...  test cases generated via the random path, the random search, the covering new, and the DFS+covering new strategies; it took less than 60 seconds for each search strategy to detect the bug.  ... 
doi:10.1109/icse.2012.6227105 dblp:conf/icse/KimKKJ12 fatcat:covumpcwh5fbhguhkmwou2hlg4

Combining Satisfiability Solving and Heuristics to Constrained Combinatorial Interaction Testing [chapter]

Andrea Calvagna, Angelo Gargantini
2009 Lecture Notes in Computer Science  
Their performance has been assessed and contrasted also with those of random and dummy ordering strategies.  ...  Combinatorial interaction testing aims at revealing errors inside a system under test triggered by unintended interaction between values of its input parameters.  ...  of heuristic strategies for which Sect. 4 reports and discusses results of their experimental evaluation.  ... 
doi:10.1007/978-3-642-02949-3_4 fatcat:hvm2m2ocnfc7hnotvnckvel6j4

Efficient Active Automata Learning via Mutation Testing

Bernhard K. Aichernig, Martin Tappler
2018 Journal of automated reasoning  
The evaluation of the test-suite generation is shown in Sect. 6, which is based on our implementation available at [32] . We conclude the paper in Sect. 7.  ...  The first type of query is simple to implement for learning black-box systems. It generally suffices to reset the system, execute a single test and record observations.  ...  We would also like to thank the developers of LearnLib and of the test-case generator available at [24] .  ... 
doi:10.1007/s10817-018-9486-0 fatcat:qgsww6e73ralhovr3ohwepyjoe

Coverage-Based Test Cases Selection for XACML Policies

Antonia Bertolino, Yves Le Traon, Francesca Lonetti, Eda Marchetti, Tejeddine Mouelhi
2014 2014 IEEE Seventh International Conference on Software Testing, Verification and Validation Workshops  
XACML is the de facto standard for implementing access control policies. Testing the correctness of policies is a critical task.  ...  The approach is evaluated using mutation analysis and is compared on the one side with a not-reduced test suite, on the other with random and greedy optimal test selection approaches.  ...  generation strategy that has been proven to be more effective than existing ones; considerably increasing the set of mutation operators for the evaluation of test suite effectiveness.  ... 
doi:10.1109/icstw.2014.49 dblp:conf/icst/BertolinoTLMM14 fatcat:pclzgukzjneanedswfilxwzjbm

Automated Test Input Generation for Android: Are We There Yet? [article]

Shauvik Roy Choudhary, Alessandra Gorla, Alessandro Orso
2015 arXiv   pre-print
main existing test input generation tools for Android.  ...  At this point in time, there are in fact a number of such techniques in the literature, which differ in the way they generate inputs, the strategy they use to explore the behavior of the app under test  ...  and for answering our clarification questions regarding the tool setup.  ... 
arXiv:1503.07217v2 fatcat:46ah67fszvavdglx7na526r43i

Grammar Based Directed Testing of Machine Learning Systems [article]

Sakshi Udeshi, Sudipta Chattopadhyay
2019 arXiv   pre-print
We also compare OGMA with a random test generation approach and observe that OGMA is more effective than such random test generation by up to 489%.  ...  We present, to the best of our knowledge, the first approach, which provides a systematic test framework for machine-learning systems that accepts grammar-based inputs.  ...  As a result, for such scenarios, the directed test strategy in OGMA outperforms random test generation by a significant margin (up to 489%).  ... 
arXiv:1902.10027v3 fatcat:duzqctcylfatjm3jz35bllh6fy
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