More Like this

1. A Characterization Study of Merge Conflicts in Java Projects

   https://doi.org/10.1145/3546944  

   Shen, Bowen ; Gulzar, Muhammad Ali ; He, Fei ; Meng, Na ( April 2023 , ACM Transactions on Software Engineering and Methodology)

   In collaborative software development, programmers create software branches to add features and fix bugs tentatively, and then 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 such conflicts. Prior work proposed tools to detect and solve conflicts. They investigate how conflicts relate to code smells and the software development process. However, many questions are still not fully investigated, such as what types of conflicts exist in real-world applications and how developers or tools handle them. For this article, we used automated textual merge, compilation, and testing to reveal three 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 from different angles.

   Our analysis revealed three interesting phenomena. First, higher-order conflicts (i.e., build and test conflicts) are harder to detect and resolve, while existing tools mainly focus on textual conflicts. Second, developers manually resolved most higher-order conflicts by applying similar edits to multiple program locations; their conflict resolutions share common editing patterns implying great opportunities for future tool design. Third, developers resolved 64% of true textual conflicts by keeping complete edits from either a left or right branch. Unlike prior studies, our research for the first time thoroughly characterizes three types of conflicts, with a special focus on higher-order conflicts and limitations of existing tool design. Our work will shed light on future research of software merge.

    

   more » « less
2. Operation-Based Refactoring-Aware Merging: An Empirical Evaluation

   https://doi.org/10.1109/TSE.2022.3228851  

   Ellis, Max ; Nadi, Sarah ; Dig, Danny ( April 2023 , IEEE Transactions on Software Engineering)

   Dealing with merge conflicts in version control systems is a challenging task for software developers. Resolving merge conflicts is a time-consuming and error-prone process, which distracts developers from important tasks. Recent work shows that refactorings are often involved in merge conflicts and that refactoring-related conflicts tend to be larger, making them harder to resolve. In the literature, there are two refactoring-aware merging techniques that claim to automatically resolve refactoring-related conflicts; however, these two techniques have never been empirically compared. In this paper, we present RefMerge, a rejuvenated Java-based design and implementation of the first technique, which is an operation-based refactoring-aware merging algorithm. We compare RefMerge to Git and the state-of-the-art graph-based refactoring-aware merging tool, IntelliMerge, on 2,001 merge scenarios with refactoring-related conflicts from 20 open-source projects. We find that RefMerge resolves or reduces conflicts in 497 (25%) merge scenarios while increasing conflicting LOC in only 214 (11%) scenarios. On the other hand, we find that IntelliMerge resolves or reduces conflicts in 478 (24%) merge scenarios but increases conflicting LOC in 597 (30%) merge scenarios. We additionally conduct a qualitative analysis of the differences between the three merging algorithms and provide insights of the strengths and weaknesses of each tool. We find that while IntelliMerge does well with ordering and formatting conflicts, it struggles with class-level refactorings and scenarios with several refactorings. On the other hand, RefMerge is resilient to the number of refactorings in a merge scenario, but we find that RefMerge introduces conflicts when inverting move-related refactorings. 

   more » « less
3. RemedioT: Remedial Actions for Internet-of-Things Conflicts

   https://doi.org/10.1145/3360322.3360837  

   Liu, Renju ; Wang, Ziqi ; Garcia, Luis ; Srivastava, Mani ( November 2019 , BuildSys '19: Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportatio)

   The increasing complexity and ubiquity of using IoT devices exacerbate the existing programming challenges in smart environments such as smart homes, smart buildings, and smart cities. Recent works have focused on detecting conflicts for the safety and utility of IoT applications, but they usually do not emphasize any means for conflict resolution other than just reporting the conflict to the application user and blocking the conflicting behavior. We propose RemedIoT, a remedial action 1 framework for resolving Internet-of-Things conflicts. The RemedIoT framework uses state of the art techniques to detect if a conflict exists in a given set of distributed IoT applications with respect to a set of policies, i.e., rules that define the allowable and restricted state-space transitions of devices. For each identified conflict, RemedIoT will suggest a set of remedial actions to the user by leveraging RemedIoT's programming abstractions. These programming abstractions enable different realizations of an IoT module while safely providing the same level of utility, e.g., if an air-conditioner application that is used to implement a cooling module conflicts with a CO2 monitor application that requires ventilation at home, a non-conflicting smart fan application will be suggested to the user. We evaluate RemedIoT on Samsung SmartThings applications and IFTTT applets and show that for 102 detected conflicts across 74 sample applications with 11 policies, RemedIoT is able to remediate ~ 80% of the conflicts found in the environment, which would normally be blocked by prior solutions. We further demonstrate the efficacy and scalability of our approach for smart city environments. 

   more » « less
4. Resolving Conflicting Insights in Asynchronous Collaborative Visual Analysis

   https://doi.org/10.1111/cgf.13997  

   Li, Jianping Kelvin ; Xu, Shenyu ; Chris) Ye, Yecong ; Ma, Kwan‐Liu ( July 2020 , Computer Graphics Forum)

   Analyzing large and complex datasets for critical decision making can benefit from a collective effort involving a team of analysts. However, insights and findings from different analysts are often incomplete, disconnected, or even conflicting. Most existing analysis tools lack proper support for examining and resolving the conflicts among the findings in order to consolidate the results of collaborative data analysis. In this paper, we present CoVA, a visual analytics system incorporating conflict detection and resolution for supporting asynchronous collaborative data analysis. By using a declarative visualization language and graph representation for managing insights and insight provenance, CoVA effectively leverages distributed revision control workflow from software engineering to automatically detect and properly resolve conflicts in collaborative analysis results. In addition, CoVA provides an effective visual interface for resolving conflicts as well as combining the analysis results. We conduct a user study to evaluate CoVA for collaborative data analysis. The results show that CoVA allows better understanding and use of the findings from different analysts.

    

   more » « less
5. BranchNet: A Convolutional Neural Network to Predict Hard-To-Predict Branches

   https://doi.org/10.1109/MICRO50266.2020.00022  

   Zangeneh, Siavash ; Pruett, Stephen ; Lym, Sangkug ; Patt, Yale N. ( October 2020 , 53rd Annual IEEE/ACM International Symposium on Microarchitecture)

   null (Ed.)

   Abstract—The state-of-the-art branch predictor, TAGE, re- mains inefficient at identifying correlated branches deep in a noisy global branch history. We argue this inefficiency is a fundamental limitation of runtime branch prediction and not a coincidental artifact due to the design of TAGE. To further improve branch prediction, we need to relax the constraint of runtime only training and adopt more sophisticated prediction mechanisms. To this end, Tarsa et al. proposed using convo- lutional neural networks (CNNs) that are trained at compile- time to accurately predict branches that TAGE cannot. Given enough profiling coverage, CNNs learn input-independent branch correlations that can accurately predict branches when running a program with unseen inputs. We build on their work and introduce BranchNet, a CNN with a practical on-chip inference engine tailored to the needs of branch prediction. At runtime, BranchNet predicts a few hard-to-predict branches, while TAGE- SC-L predicts the remaining branches. This hybrid approach reduces the MPKI of SPEC2017 Integer benchmarks by 7.6% (and up to 15.7%) when compared to a very large (impractical) MTAGE-SC baseline, demonstrating a fundamental advantage in the prediction capabilities of BranchNet compared to TAGE- like predictors. We also propose a practical resource-constrained variant of BranchNet that improves the MPKI by 9.6% (and up to 17.7%) compared to a 64KB TAGE-SC-L without increasing the prediction latency. 

   more » « less