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1. CirFix: Automatically Repairing Defects in Hardware Design Code

   Hammad Ahmad; Yu Huang; Westley Weimer (February 2022, Architectural Support for Programming Languages and Operating Systems (ASPLOS))

   This paper presents CirFix, a framework for automatically repairing defects in hardware designs implemented in languages like Verilog. We propose a novel fault localization approach based on assignments to wires and registers, and a fitness function tailored to the hardware domain to bridge the gap between software-level automated program repair and hardware descriptions. We also present a benchmark suite of 32 defect scenarios corresponding to a variety of hardware projects. Overall, CirFix produces plausible repairs for 21/32 and correct repairs for 16/32 of the defect scenarios. This repair rate is comparable to that of successful program repair approaches for software, indicating CirFix is effective at bringing over the benefits of automated program repair to the hardware domain for the first time. 

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2. SOSRepair: Expressive Semantic Search for Real-World Program Repair

   https://doi.org/10.1109/TSE.2019.2944914  

   Afzal, Afsoon; Motwani, Manish; Stolee, Kathryn; Brun, Yuriy; Le Goues, Claire (January 2020, IEEE Transactions on Software Engineering)

   Automated program repair holds the potential to significantly reduce software maintenance effort and cost. However, recent studies have shown that it often produces low-quality patches that repair some but break other functionality. We hypothesize that producing patches by replacing likely faulty regions of code with semantically-similar code fragments, and doing so at a higher level of granularity than prior approaches can better capture abstraction and the intended specification, and can improve repair quality. We create SOSRepair, an automated program repair technique that uses semantic code search to replace candidate buggy code regions with behaviorally-similar (but not identical) code written by humans. SOSRepair is the first such technique to scale to real-world defects in real-world systems. On a subset of the ManyBugs benchmark of such defects, SOSRepair produces patches for 23 (35%) of the 65 defects, including 3, 5, and 8 defects for which previous state-of-the-art techniques Angelix, Prophet, and GenProg do not, respectively. On these 23 defects, SOSRepair produces more patches (8, 35%) that pass all independent tests than the prior techniques. We demonstrate a relationship between patch granularity and the ability to produce patches that pass all independent tests. We then show that fault localization precision is a key factor in SOSRepair's success. Manually improving fault localization allows SOSRepair to patch 24 (37%) defects, of which 16 (67%) pass all independent tests. We conclude that (1) higher-granularity, semantic-based patches can improve patch quality, (2) semantic search is promising for producing high-quality real-world defect repairs, (3) research in fault localization can significantly improve the quality of program repair techniques, and (4) semi-automated approaches in which developers suggest fix locations may produce high-quality patches. 

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3. DeepFL: integrating multiple fault diagnosis dimensions for deep fault localization

   https://doi.org/10.1145/3293882.3330574  

   Li, Xia; Li, Wei; Zhang, Yuqun; Zhang, Lingming (January 2019, ACM SIGSOFT International Symposium on Software Testing and Analysis)

   Learning-based fault localization has been intensively studied recently. Prior studies have shown that traditional Learning-to-Rank techniques can help precisely diagnose fault locations using various dimensions of fault-diagnosis features, such as suspiciousness values computed by various off-the-shelf fault localization techniques. However, with the increasing dimensions of features considered by advanced fault localization techniques, it can be quite challenging for the traditional Learning-to-Rank algorithms to automatically identify effective existing/latent features. In this work, we propose DeepFL, a deep learning approach to automatically learn the most effective existing/latent features for precise fault localization. Although the approach is general, in this work, we collect various suspiciousness-value-based, fault-proneness-based and textual-similarity-based features from the fault localization, defect prediction and information retrieval areas, respectively. DeepFL has been studied on 395 real bugs from the widely used Defects4J benchmark. The experimental results show DeepFL can significantly outperform state-of-the-art TraPT/FLUCCS (e.g., localizing 50+ more faults within Top-1). We also investigate the impacts of deep model configurations (e.g., loss functions and epoch settings) and features. Furthermore, DeepFL is also surprisingly effective for cross-project prediction. 

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4. Tolerating Defects in Low-Power Neural Network Accelerators Via Retraining-Free Weight Approximation

   https://doi.org/10.1145/3477016  

   Hosseini, Fateme S.; Meng, Fanruo; Yang, Chengmo; Wen, Wujie; Cammarota, Rosario (October 2021, ACM Transactions on Embedded Computing Systems)

   null (Ed.)

   Hardware accelerators are essential to the accommodation of ever-increasing Deep Neural Network (DNN) workloads on the resource-constrained embedded devices. While accelerators facilitate fast and energy-efficient DNN operations, their accuracy is threatened by faults in their on-chip and off-chip memories, where millions of DNN weights are held. The use of emerging Non-Volatile Memories (NVM) further exposes DNN accelerators to a non-negligible rate of permanent defects due to immature fabrication, limited endurance, and aging. To tolerate defects in NVM-based DNN accelerators, previous work either requires extra redundancy in hardware or performs defect-aware retraining, imposing significant overhead. In comparison, this paper proposes a set of algorithms that exploit the flexibility in setting the fault-free bits in weight memory to effectively approximate weight values, so as to mitigate defect-induced accuracy drop. These algorithms can be applied as a one-step solution when loading the weights to embedded devices. They only require trivial hardware support and impose negligible run-time overhead. Experiments on popular DNN models show that the proposed techniques successfully boost inference accuracy even in the face of elevated defect rates in the weight memory. 

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5. Decomposition of strongly charged topological defects

   Kralj, S.; Murray, B.; Rosenblatt, C. (April 2017, Physical review. E, Statistical, nonlinear and soft matter physics)

   We study decomposition of geometrically enforced nematic topological defects bearing relatively large defect strengths m in effectively two-dimensional planar systems. Theoretically, defect cores are analyzed within the mesoscopic Landau - De Gennes approach in terms of the tensor nematic order parameter. We demonstrate a robust tendency of defect decomposition into elementary units where two qualitatively different scenarios imposing total defect strengths to a nematic region are employed. Some theoretical predictions are verified experimentally, where arrays of defects bearing charges m = ±1, and even m = ±2, are enforced within a plane-parallel nematic cell using an AFM scribing method. 

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