Browsing by Author "Shen, Bowen"

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    A Characterization Study of Merge Conflicts in Java Projects
    Shen, Bowen; Gulzar, Muhammad Ali; He, Fei; Meng, Na (ACM, 2022)
    In collaborative software development, programmers create branches to add features and fix bugs, and merge branches to integrate edits. When edits from different branches textually overlap (i.e., textual conflicts) or lead to compilation and runtime errors (i.e., build and test conflicts), it is challenging for developers to remove conflicts. Prior work proposed tools to detect and solve conflicts. However, many questions are not fully investigated, such as what types of conflicts exist in practice and how developers or tools handle them. For this paper, we used automated textual merge, compilation, and testing to reveal 3 types of conflicts in 208 open-source repositories: textual conflicts, build conflicts (i.e., conflicts causing build errors), and test conflicts (i.e., conflicts triggering test failures). We manually inspected 538 conflicts and their resolutions to characterize merge conflicts. Our analysis revealed three phenomena. First, higher-order conflicts (i.e., build and test conflicts) are harder to handle, while existing tools mainly focus on textual conflicts. Second, developers resolved most higher-order conflicts by applying similar edits to multiple program locations. Third, developers resolved 64% of true textual conflicts by keeping complete edits from either a left or right branch. Our work will shed light on future research of software merge.
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    Detecting Build Conflicts in Software Merge for Java Programs via Static Analysis
    Towqir, Sheikh Shadab; Shen, Bowen; Gulzar, Muhammad Ali; Meng, Na (ACM, 2022-10-10)
    In software merge, the edits from different branches can textually overlap (i.e., textual conflicts) or cause build and test errors (i.e., build and test conflicts), jeopardizing programmer productivity and software quality. Existing tools primarily focus on textual conflicts; few tools detect higher-order conflicts (i.e., build and test conflicts). However, existing detectors of build conflicts are limited. Due to their heavy usage of automatic build, current detectors (e.g., Crystal) only report build errors instead of identifying the root causes; developers have to manually locate conflicting edits. These detectors only help when the branches-to-merge have no textual conflict. We present a new static analysis-based approach Bucond (“build conflict detector”). Given three code versions in a merging scenario: base b, left l , and right r , Bucond models each version as a graph, and compares graphs to extract entity-related edits (e.g., class renaming) in l and r . We believe that build conflicts occur when certain edits are co-applied to related entities between branches. Bucond realizes this insight via pattern matching to identify any cross-branch edit combination that can trigger build conflicts (e.g., one branch adds a reference to field F while the other branch removes F). We systematically explored and devised 57 patterns, covering 97% of the build conflicts in our experiments. Our evaluation shows Bucond to complement build-based detectors, as it (1) detects conflicts with 100% precision and 88%–100% recall, (2) locates conflicting edits, and (3) works well when those detectors do not.
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    Diagnostic interchangeability of deep convolutional neural networks reconstructed knee MR images: preliminary experience
    Subhas, Naveen; Li, Hongyu; Yang, Mingrui; Winalski, Carl S.; Polster, Joshua; Obuchowski, Nancy; Mamoto, Kenji; Liu, Ruiying; Zhang, Chaoyi; Huang, Peizhou; Gaire, Sunil Kumar; Liang, Dong; Shen, Bowen; Xiaojuan, Li; Ying, Leslie (2020-09)
    Background: MRI acceleration using deep learning (DL) convolutional neural networks (CNNs) is a novel technique with great promise. Increasing the number of convolutional layers may allow for more accurate image reconstruction. Studies on evaluating the diagnostic interchangeability of DL reconstructed knee magnetic resonance (MR) images are scarce. The purpose of this study was to develop a deep CNN (DCNN) with an optimal number of layers for accelerating knee magnetic resonance imaging (MRI) acquisition by 6-fold and to test the diagnostic interchangeability and image quality of nonaccelerated images versus images reconstructed with a 15-layer DCNN or 3-layer CNN. Methods: For the feasibility portion of this study, 10 patients were randomly selected from the Osteoarthritis Initiative (OAI) cohort. For the interchangeability portion of the study, 40 patients were randomly selected from the OAI cohort. Three readers assessed meniscal and anterior cruciate ligament (ACL) tears and cartilage defects using DCNN, CNN, and nonaccelerated images. Image quality was subjectively graded as nondiagnostic, poor, acceptable, or excellent. Interchangeability was tested by comparing the frequency of agreement when readers used both accelerated and nonaccelerated images to frequency of agreement when readers only used nonaccelerated images. A noninferiority margin of 0.10 was used to ensure type I error <= 5% and power >= 80%. A logistic regression model using generalized estimating equations was used to compare proportions; 95% confidence intervals (CIs) were constructed. Results: DCNN and CNN images were interchangeable with nonaccelerated images for all structures, with excess disagreement values ranging from -2.5% [95% CI: (-6.1, 1.1)] to 3.0% [95% CI: (-0.1, 6.1)]. The quality of DCNN images was graded higher than that of CNN images but less than that of nonaccelerated images [excellent/acceptable quality: DCNN, 95% of cases (114/120); CNN, 60% (72/120); nonaccelerated, 97.5% (117/120)]. Conclusions: Six-fold accelerated knee images reconstructed with a DL technique are diagnostically interchangeable with nonaccelerated images and have acceptable image quality when using a 15-layer CNN.