Abstract: System and method for recommending static analysis fixes in a computer program include: receiving violations from a static analysis tool and the meta data related to the violations; for each violation, encoding a code snippet in which said each violation is located into a first vector and encoding meta-data associated with each violation into a second vector; for each violation, concatenating the first vector and the second vector into a single vector; generating a utility matrix from the single vectors, where values in each cell correspond to a certain user's action on one of the violations; revising the utility matrix, based on user selection of selected violations of the plurality of violations; and executing a matrix factorization process to build intermediate representations of users and sets of violations to update the utility matrix as an updated utility matrix to obtain predictions for missing values, as recommendation to the user.

Abstract: System and method for recommending static analysis fixes in a computer program include: receiving violations from a static analysis tool and the meta data related to the violations; for each violation, encoding a code snippet in which said each violation is located into a first vector and encoding meta-data associated with each violation into a second vector; for each violation, concatenating the first vector and the second vector into a single vector; generating a utility matrix from the single vectors, where values in each cell correspond to a certain user's action on one of the violations; revising the utility matrix, based on user selection of selected violations of the plurality of violations; and executing a matrix factorization process to build intermediate representations of users and sets of violations to update the utility matrix as an updated utility matrix to obtain predictions for missing values, as recommendation to the user.

Abstract: Computer-implemented method for improving the test results of a static code analyzer includes: receiving a plurality of test violations from the static code analyzer; constructing a first vector for each code snippet containing error candidates; constructing a second vector for each test violation from meta-data associated with said each test violation; merging the first vector with the second vector to produce a feature vector for each test violation; and applying statistical learning techniques to each feature vector to estimate a probability that an error candidate in the plurality of test violations is a true error or a false positive.

Abstract: Computer-implemented method for improving the test results of a static code analyzer includes: receiving a plurality of test violations from the static code analyzer; constructing a first vector for each code snippet containing error candidates; constructing a second vector for each test violation from meta-data associated with said each test violation; merging the first vector with the second vector to produce a feature vector for each test violation; and applying statistical learning techniques to each feature vector to estimate a probability that an error candidate in the plurality of test violations is a true error or a false positive.