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1. Model-driven Per-panel Solar Anomaly Detection for Residential Arrays

   https://doi.org/10.1145/3460236  

   Feng, Menghong ; Bashir, Noman ; Shenoy, Prashant ; Irwin, David ; Kosanovic, Beka ( October 2021 , ACM Transactions on Cyber-Physical Systems)

   null (Ed.)

   There has been significant growth in both utility-scale and residential-scale solar installations in recent years, driven by rapid technology improvements and falling prices. Unlike utility-scale solar farms that are professionally managed and maintained, smaller residential-scale installations often lack sensing and instrumentation for performance monitoring and fault detection. As a result, faults may go undetected for long periods of time, resulting in generation and revenue losses for the homeowner. In this article, we present SunDown, a sensorless approach designed to detect per-panel faults in residential solar arrays. SunDown does not require any new sensors for its fault detection and instead uses a model-driven approach that leverages correlations between the power produced by adjacent panels to detect deviations from expected behavior. SunDown can handle concurrent faults in multiple panels and perform anomaly classification to determine probable causes. Using two years of solar generation data from a real home and a manually generated dataset of multiple solar faults, we show that SunDown has a Mean Absolute Percentage Error of 2.98% when predicting per-panel output. Our results show that SunDown is able to detect and classify faults, including from snow cover, leaves and debris, and electrical failures with 99.13% accuracy, and can detect multiple concurrent faults with 97.2% accuracy. 

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2. Using Search-Based Test Generation to Discover Real Faults in Guava

   https://doi.org/10.1007/978-3-319-66299-2_13  

   Almulla, Hussein ; Salahirad, Alireza ; Gay, Gregory ( August 2017 , International Symposium on Search Based Software Engineering)

   Testing costs can be reduced through automated unit test generation. An important benchmark for such tools is their ability to detect real faults. Fault databases, such as Defects4J, assist in this task. The Guava project - a collection of Java libraries from Google - offers an opportunity to expand such databases with additional complex faults. We have identified 11 faults in the Guava project, added them to Defects4J, and assessed the ability of the EvoSuite framework to detect these faults. Ultimately, EvoSuite was able to detect three faults. Analysis of the remaining faults offers lessons in how to improve generation tools. We offer these faults to the community to assist future benchmarking efforts. 

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3. To call, or not to call: contrasting direct and indirect branch coverage in test generation

   https://doi.org/10.1145/3194718.3194719  

   Gay, Gregory ( January 2018 , SBST '18 Proceedings of the 11th International Workshop on Search-Based Software Testing)

   While adequacy criteria offer an end-point for testing, they do not mandate how targets are covered. Branch Coverage may be attained through direct calls to methods, or through indirect calls between methods. Automated generation is biased towards the rapid gains offered by indirect coverage. Therefore, even with the same end-goal, humans and automation produce very different tests. Direct coverage may yield tests that are more understandable, and that detect faults missed by traditional approaches. However, the added burden for the generation framework may result in lower coverage and faults that emerge through method interactions may be missed. To compare the two approaches, we have generated test suites for both, judging efficacy against real faults. We have found that requiring direct coverage results in lower achieved coverage and likelihood of fault detection. However, both forms of Branch Coverage cover code and detect faults that the other does not. By isolating methods, Direct Branch Coverage is less constrained in the choice of input. However, traditional Branch Coverage is able to leverage method interactions to discover faults. Ultimately, both are situationally applicable within the context of a broader testing strategy. 

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4. SunDown: Model-driven Per-Panel Solar Anomaly Detection for Residential Arrays

   https://doi.org/10.1145/3378393.3402257  

   Feng, Menghong ; Bashir, Noman ; Shenoy, Prashant ; Irwin, David ; Kosanovic, Dragoljub ( June 2020 , Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS))

   null (Ed.)

   Solar arrays often experience faults that go undetected for long periods of time, resulting in generation and revenue losses. In this paper, we present SunDown, a sensorless approach for detecting per-panel faults in solar arrays. SunDown's model-driven approach leverages correlations between the power produced by adjacent panels to detect deviations from expected behavior, can handle concurrent faults in multiple panels, and performs anomaly classification to determine probable causes. Using two years of solar data from a real home and a manually generated dataset of solar faults, we show that our approach is able to detect and classify faults, including from snow, leaves and debris, and electrical failures with 99.13% accuracy, and can detect concurrent faults with 97.2% accuracy. 

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5. Learning How to Search: Generating Exception-Triggering Tests Through Adaptive Fitness Function Selection

   Hussein Almulla, Gregory Gay ( March 2020 , 13th IEEE International Conference on Software Testing, Validation and Verification)

   Search-based test generation is guided by feedback from one or more fitness functions—scoring functions that judge solution optimality. Choosing informative fitness functions is crucial to meeting the goals of a tester. Unfortunately, many goals—such as forcing the class-under-test to throw exceptions— do not have a known fitness function formulation. We propose that meeting such goals requires treating fitness function identification as a secondary optimization step. An adaptive algorithm that can vary the selection of fitness functions could adjust its selection throughout the generation process to maximize goal attainment, based on the current population of test suites. To test this hypothesis, we have implemented two reinforcement learning algorithms in the EvoSuite framework, and used these algorithms to dynamically set the fitness functions used during generation. We have evaluated our framework, EvoSuiteFIT, on a set of 386 real faults. EvoSuiteFIT discovers and retains more exception-triggering input and produces suites that detect a variety of faults missed by the other techniques. The ability to adjust fitness functions allows EvoSuiteFIT to make strategic choices that efficiently produce more effective test suites. 

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