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1. A Large Scale Test Corpus for Semantic Table Search

   https://doi.org/10.1145/3626772.3657877  

   Leventidis, Aristotelis; Christensen, Martin Pekár; Lissandrini, Matteo; Di_Rocco, Laura; Hose, Katja; Miller, Renée J (July 2024, ACM)

   Table search aims to answer a query with a ranked list of tables. Unfortunately, current test corpora have focused mostly on needle- in-the-haystack tasks, where only a few tables are expected to exactly match the query intent. Instead, table search tasks often arise in response to the need for retrieving new datasets or augment- ing existing ones, e.g., for data augmentation within data science or machine learning pipelines. Existing table repositories and bench- marks are limited in their ability to test retrieval methods for table search tasks. Thus, to close this gap, we introduce a novel dataset for query-by-example Semantic Table Search. This novel dataset con- sists of two snapshots of the large-scale Wikipedia tables collection from 2013 and 2019 with two important additions: (1) a page and topic aware ground truth relevance judgment and (2) a large-scale DBpedia entity linking annotation. Moreover, we generate a novel set of entity-centric queries that allows testing existing methods under a novel search scenario: semantic exploratory search. The resulting resource consists of 9,296 novel queries, 610,553 query- table relevance annotations, and 238,038 entity-linked tables from the 2013 snapshot. Similarly, on the 2019 snapshot, the resource consists of 2,560 queries, 958,214 relevance annotations, and 457,714 total tables. This makes our resource the largest annotated table- search corpus to date (97 times more queries and 956 times more annotated tables than any existing benchmark). We perform a user study among domain experts and prove that these annotators agree with the automatically generated relevance annotations. As a re- sult, we can re-evaluate some basic assumptions behind existing table search approaches identifying their shortcomings along with promising novel research directions. 

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2. Misconfiguration Software Testing for Failure Emergence in Autonomous Driving Systems

   https://doi.org/10.1145/3660792  

   Chen, Yuntianyi; Huai, Yuqi; Li, Shilong; Hong, Changnam; Garcia, Joshua (July 2024, Proceedings of the ACM on Software Engineering)

   The optimization of a system’s configuration options is crucial for determining its performance and functionality, particularly in the case of autonomous driving software (ADS) systems because they possess a multitude of such options. Research efforts in the domain of ADS have prioritized the development of automated testing methods to enhance the safety and security of self-driving cars. Presently, search-based approaches are utilized to test ADS systems in a virtual environment, thereby simulating real-world scenarios. However, such approaches rely on optimizing the waypoints of ego cars and obstacles to generate diverse scenarios that trigger violations, and no prior techniques focus on optimizing the ADS from the perspective of configuration. To address this challenge, we present a framework called ConfVE, which is the first automated configuration testing framework for ADSes. ConfVE’s design focuses on the emergence of violations through rerunning scenarios generated by different ADS testing approaches under different configurations, leveraging 9 test oracles to enable previous ADS testing approaches to find more types of violations without modifying their designs or implementations and employing a novel technique to identify bug-revealing violations and eliminate duplicate violations. Our evaluation results demonstrate that ConfVE can discover 1,818 unique violations and reduce 74.19% of duplicate violations. 

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3. Multifaceted test suite generation using primary and supporting fitness functions

   https://doi.org/10.1145/3194718.3194723  

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

   Dozens of criteria have been proposed to judge testing adequacy. Such criteria are important, as they guide automated generation efforts. Yet, the current use of such criteria in automated generation contrasts how such criteria are used by humans. For a human, coverage is part of a multifaceted combination of testing strategies. In automated generation, coverage is typically the goal, and a single fitness function is applied at one time. We propose that the key to improving the fault detection efficacy of search-based test generation approaches lies in a targeted, multifaceted approach pairing primary fitness functions that effectively explore the structure of the class under test with lightweight supporting fitness functions that target particular scenarios likely to trigger an observable failure. This report summarizes our findings to date, details the hypothesis of primary and supporting fitness functions, and identifies outstanding research challenges related to multifaceted test suite generation. We hope to inspire new advances in search-based test generation that could benefit our software-powered society. 

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4. Automating Failure Testing Research at Internet Scale

   https://doi.org/10.1145/2987550.2987555  

   Alvaro, Peter; Andrus, Kolton; Sanden, Chris; Rosenthal, Casey; Basiri, Ali; Hochstein, Lorin (January 2016, Proceedings of the Seventh ACM Symposium on Cloud Computing)

   Large-scale distributed systems must be built to anticipate and mitigate a variety of hardware and software failures. In order to build confidence that fault-tolerant systems are correctly implemented, Netflix (and similar enterprises) regularly run failure drills in which faults are deliberately injected in their production system. The combinatorial space of failure scenarios is too large to explore exhaustively. Existing failure testing approaches either randomly explore the space of potential failures randomly or exploit the "hunches" of domain experts to guide the search. Random strategies waste resources testing "uninteresting" faults, while programmer-guided approaches are only as good as human intuition and only scale with human effort. In this paper, we describe how we adapted and implemented a research prototype called lineage-driven fault injection (LDFI) to automate failure testing at Netflix. Along the way, we describe the challenges that arose adapting the LDFI model to the complex and dynamic realities of the Netflix architecture. We show how we implemented the adapted algorithm as a service atop the existing tracing and fault injection infrastructure, and present early results. 

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5. KCRL: A Prior Knowledge Based Causal Discovery Framework With Reinforcement Learning

   Hasan, Uzma; Gani, Md Osman (April 2022, Proceedings of Machine Learning Research)

   Causal discovery is an important problem in many sciences that enables us to estimate causal relationships from observational data. Particularly, in the healthcare domain, it can guide practitioners in making informed clinical decisions. Several causal discovery approaches have been developed over the last few decades. The success of these approaches mostly relies on a large number of data samples. In practice, however, an infinite amount of data is never available. Fortunately, often we have some prior knowledge available from the problem domain. Particularly, in healthcare settings, we often have some prior knowledge such as expert opinions, prior RCTs, literature evidence, and systematic reviews about the clinical problem. This prior information can be utilized in a systematic way to address the data scarcity problem. However, most of the existing causal discovery approaches lack a systematic way to incorporate prior knowledge during the search process. Recent advances in reinforcement learning techniques can be explored to use prior knowledge as constraints by penalizing the agent for their violations. Therefore, in this work, we propose a framework KCRL that utilizes the existing knowledge as a constraint to penalize the search process during causal discovery. This utilization of existing information during causal discovery reduces the graph search space and enables a faster convergence to the optimal causal mechanism. We evaluated our framework on benchmark synthetic and real datasets as well as on a real-life healthcare application. We also compared its performance with several baseline causal discovery methods. The experimental findings show that penalizing the search process for constraint violation yields better performance compared to existing approaches that do not include prior knowledge. 

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