Cascade is a classic yet powerful architecture that has boosted performance on various tasks. However, how to introduce cascade to instance segmentation remains an open question. A simple combination of Cascade R-CNN and Mask R-CNN only brings limited gain. In exploring a more effective approach, we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation. In this work, we propose a new framework, Hybrid Task Cascade (HTC), which differs in two important aspects: (1) instead of performing cascaded refinement on these two tasks separately, it interweaves them for a joint multi-stage processing; (2) it adopts a fully convolutional branch to provide spatial context, which can help distinguishing hard foreground from cluttered background. Overall, this framework can learn more discriminative features progressively while integrating complementary features together in each stage. Without bells and whistles, a single HTC obtains 38.4% and 1.5% improvement over a strong Cascade Mask R-CNN baseline on MSCOCO dataset. Moreover, our overall system achieves 48.6 mask AP on the test-challenge split, ranking 1st in the COCO 2018 Challenge Object Detection Task. Code is available at: https://github.com/ open-mmlab/mmdetection.

Due to the difficulty of repairing defect, many research efforts have been devoted into automatic defect repair. Given a buggy program that fails some test cases, a typical automatic repair technique tries to modify the program to make all tests pass. However, since the test suites in real world projects are usually insufficient, aiming at passing the test suites often leads to incorrect patches. This problem is known as weak test suites or overfitting.In this paper we aim to produce precise patches, that is, any patch we produce has a relatively high probability to be correct. More concretely, we focus on condition synthesis, which was shown to be able to repair more than half of the defects in existing approaches. Our key insight is threefold. First, it is important to know what variables in a local context should be used in an "if" condition, and we propose a sorting method based on the dependency relations between variables. Second, we observe that the API document can be used to guide the repair process, and propose document analysis technique to further filter the variables. Third, it is important to know what predicates should be performed on the set of variables, and we propose to mine a set of frequently used predicates in similar contexts from existing projects.Based on the insight, we develop a novel program repair system, ACS, that could generate precise conditions at faulty locations. Furthermore, given the generated conditions are very precise, we can perform a repair operation that is previously deemed to be too overfitting: directly returning the test oracle to repair the defect. Using our approach, we successfully repaired 17 defects on four projects of Defects4J, which is the largest number of fully automatically repaired defects reported on the dataset so far. More importantly, the precision of our approach in the evaluation is 73.9%, which is significantly higher than previous approaches, which are usually less than 40%.

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Test-based automatic program repair has attracted a lot of attention in recent years. However, the test suites in practice are often too weak to guarantee correctness and existing approaches often generate a large number of incorrect patches.To reduce the number of incorrect patches generated, we propose a novel approach that heuristically determines the correctness of the generated patches. The core idea is to exploit the behavior similarity of test case executions. The passing tests on original and patched programs are likely to behave similarly while the failing tests on original and patched programs are likely to behave differently. Also, if two tests exhibit similar runtime behavior, the two tests are likely to have the same test results. Based on these observations, we generate new test inputs to enhance the test suites and use their behavior similarity to determine patch correctness.Our approach is evaluated on a dataset consisting of 139 patches generated from existing program repair systems including jGen-Prog, Nopol, jKali, ACS and HDRepair. Our approach successfully prevented 56.3% of the incorrect patches to be generated, without blocking any correct patches.

Abstract-To deal with post-release bugs, many software projects set up public bug repositories for users all over the world to report bugs that they have encountered. Recently, researchers have proposed various information retrieval based approaches to localizing faults based on bug reports. In these approaches, source files are processed as single units, where noise in large files may affect the accuracy of fault localization. Furthermore, bug reports often contain stack-trace information, but existing approaches often treat this information as plain text. In this paper, we propose to use segmentation and stack-trace analysis to improve the performance of bug localization. Specifically, given a bug report, we divide each source code file into a series of segments and use the segment most similar to the bug report to represent the file. We also analyze the bug report to identify possible faulty files in a stack trace and favor these files in our retrieval. According to our empirical results, our approach is able to significantly improve BugLocator, a representative fault localization approach, on all the three software projects (i.e., Eclipse, AspectJ, and SWT) used in our empirical evaluation. Furthermore, segmentation and stack-trace analysis are complementary to each other for boosting the performance of bug-report-oriented fault localization.

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