Previous work has shown that early resolution of issues detected by static code analyzers can prevent major costs later on. However, developers often ignore such issues for two main reasons. First, many issues should be interpreted to determine if they correspond to actual flaws in the program. Second, static analyzers often do not present the issues in a way that is actionable. To address these problems, we present Sorald: a novel system that uses metaprogramming templates to transform the abstract syntax trees of programs and suggests fixes for static analysis warnings. Thus, the burden on the developer is reduced from interpreting and fixing static issues, to inspecting and approving full fledged solutions. Sorald fixes violations of 10 rules from SonarJava, one of the most widely used static analyzers for Java. We evaluate Sorald on a dataset of 161 popular repositories on Github. Our analysis shows the effectiveness of Sorald as it fixes 65% (852/1,307) of the violations that meets the repair preconditions. Overall, our experiments show it is possible to automatically fix notable violations of the static analysis rules produced by the state-of-the-art static analyzer SonarJava.

This paper focuses on the development of a novel algorithm for deriving snowpack density over the snow-covered region of the Himalayas. The analysis utilizes fully polarimetric TerraSAR-X synthetic aperture radar data sets, field observations, and other ancillary information for the retrieval of snowpack density. The algorithm involves the development of a new generalized hybrid decomposition model. The generalized volume scattering parameter from the decomposition model is inverted for snow density estimation. A few field data measurements' campaigns were carried out, within near-real time of satellite passing over the area, to collect various parameters such as temperature, water content, and the density of the snowpack at varying depths. These field observations are further used for validation of the results obtained from the inversion algorithm. It is also found that the model-estimated snowpack density is highly congruent with the field-measured snowpack density. The mean absolute error of snowpack density, root-mean-square error, and index of agreement are found to be 9.9 kg/m3, 10 kg/m3, and 0.96, respectively, which are well within the acceptable range.

We address the problem of scheduling test sessions for core based systems-on-chip (SOC). We assume the built-in self-test methodology for testing individual cores and permit sharing of test resources (pattern generators and signature registers) among cores. Our objective is to minimize the test application time and the test area overhead, treating the total power dissipation as a constraint. A vast solution space exists for the problem of test scheduling. At one end of the spectrum is an entirely sequential test schedule which will consume the least test power, and at the other end of the spectrum is a fully concurrent test schedule which will consume the largest test power. Each of these solutions will differ in terms of the test area overhead and the test application time. We show a polynomial-time algorithm for finding an optimum power-constrained schedule which minimizes the test time. In our formulation, we implicitly address the problem of minimizing the test area overhead by introducing the notion of area penalty for merging the test sessions for two different cores. We argue that the merger of two test sessions for two different cores must address such issues as similarity of the cores being tested as well as layout-related issues. We capture these area penalties in the form of a desirability matrix which is the essential data structure for our scheduling algorithm. We report the results of our implementation of the scheduling algorithm on two circuits.