Software testing is difficult to automate, especially in programs which have no oracle, or method of determining which output is correct. Metamorphic testing is a solution this problem. Metamorphic testing uses metamorphic relations to define test cases and expected outputs. A large amount of time is needed for a domain expert to determine which metamorphic relations can be used to test a given program. Metamorphic relation prediction removes this need for such an expert. We propose a method using semi-supervised machine learning to detect which metamorphic relations are applicable to a given code base. We compare this semi-supervised model with a supervised model, and show that the addition of unlabeled data improves the classification accuracy of the MR prediction model.
A second paper by studies the dynamic inference of MRs @cite_0 . The authors built a tool to implement their algorithm. The algorithm works by first defining a set of MRs, which they call transformers: "multiplier", "adder", "negator", "shuffler", and "reverser". For each transformer in the set, a function is executed with and without the transformation applied. The results are compared to see if the functions exhibit a metamorphic property. In this way, they can predict which MRs apply to a given function.
@cite_0
2140678594 2949644922 2019999542 2949485285
We propose a method for automatically generating abstract transformers for static analysis by abstract interpretation. The method focuses on linear constraints on programs operating on rational, real or floating-point variables and containing linear assignments and tests. In addition to loop-free code, the same method also applies for obtaining least fixed points as functions of the precondition, which permits the analysis of loops and recursive functions. Our algorithms are based on new quantifier elimination and symbolic manipulation techniques. Given the specification of an abstract domain, and a program block, our method automatically outputs an implementation of the corresponding abstract transformer. It is thus a form of program transformation. The motivation of our work is data-flow synchronous programming languages, used for building control-command embedded systems, but it also applies to imperative and functional programming. We present the Insertion Transformer, an iterative, partially autoregressive model for sequence generation based on insertion operations. Unlike typical autoregressive models which rely on a fixed, often left-to-right ordering of the output, our approach accommodates arbitrary orderings by allowing for tokens to be inserted anywhere in the sequence during decoding. This flexibility confers a number of advantages: for instance, not only can our model be trained to follow specific orderings such as left-to-right generation or a binary tree traversal, but it can also be trained to maximize entropy over all valid insertions for robustness. In addition, our model seamlessly accommodates both fully autoregressive generation (one insertion at a time) and partially autoregressive generation (simultaneous insertions at multiple locations). We validate our approach by analyzing its performance on the WMT 2014 English-German machine translation task under various settings for training and decoding. We find that the Insertion Transformer outperforms many prior non-autoregressive approaches to translation at comparable or better levels of parallelism, and successfully recovers the performance of the original Transformer while requiring only logarithmically many iterations during decoding. If fusion rules cannot adapt to the changes of environment and individual users, multimodal systems may perform worse than unimodal systems when one or more modalities encounter data degeneration. This paper develops a robust face and ear based multimodal biometric system using Sparse Representation (SR), which integrates the face and ear at feature level, and can effectively adjust the fusion rule based on reliability difference between the modalities. We first propose a novel index called Sparse Coding Error Ratio (SCER) to measure the reliability difference between face and ear query samples. Then, SCER is utilized to develop an adaptive feature weighting scheme for dynamically reducing the negative effect of the less reliable modality. In multimodal classification phase, SR-based classification techniques are employed, i.e., Sparse Representation based Classification (SRC) and Robust Sparse Coding (RSC). Finally, we derive a category of SR-based multimodal recognition methods, including Multimodal SRC with feature Weighting (MSRCW) and Multimodal RSC with feature Weighting (MRSCW). Experimental results demonstrate that: (a) MSRCW and MRSCW perform significantly better than the unimodal recognition using either face or ear alone, as well as the known multimodal methods; (b) The effectiveness of adaptive feature weighting is verified. MSRCW and MRSCW are very robust to the image degeneration occurring to one of the modalities. Even when face (ear) query sample suffers from 100 random pixel corruption, they can still get the performance close to the ear (face) unimodal recognition; (c) By integrating the advantages of adaptive feature weighting and sparsity-constrained regression, MRSCW seems excellent in tackling the face and ear based multimodal recognition problem. This paper proves that an "old dog", namely -- the classical Johnson-Lindenstrauss transform, "performs new tricks" -- it gives a novel way of preserving differential privacy. We show that if we take two databases, @math and @math , such that (i) @math is a rank-1 matrix of bounded norm and (ii) all singular values of @math and @math are sufficiently large, then multiplying either @math or @math with a vector of iid normal Gaussians yields two statistically close distributions in the sense of differential privacy. Furthermore, a small, deterministic and alteration of the input is enough to assert that all singular values of @math are large. We apply the Johnson-Lindenstrauss transform to the task of approximating cut-queries: the number of edges crossing a @math -cut in a graph. We show that the JL transform allows us to that preserves edge differential privacy (where two graphs are neighbors if they differ on a single edge) while adding only @math random noise to any given query (w.h.p). Comparing the additive noise of our algorithm to existing algorithms for answering cut-queries in a differentially private manner, we outperform all others on small cuts ( @math ). We also apply our technique to the task of estimating the variance of a given matrix in any given direction. The JL transform allows us to that preserves differential privacy w.r.t bounded changes (each row in the matrix can change by at most a norm-1 vector) while adding random noise of magnitude independent of the size of the matrix (w.h.p). In contrast, existing algorithms introduce an error which depends on the matrix dimensions.