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1. Architectural Decay as Predictor of Issue-and Change-Proneness

   Le, Duc M.; Karthik, Suhrid; Schmitt Laser, Marcelo; Medvidovic, Nenad (March 2021, 2021 IEEE 18th International Conference on Software Architecture (ICSA))

   Jensen, Anton; Lewis, Grace A. (Ed.)

   Architectural decay imposes real costs in terms of developer effort, system correctness, and performance. Over time, those problems are likely to be revealed as explicit implementation issues (defects, feature changes, etc.). Recent empirical studies have demonstrated that there is a significant correlation between architectural “smells”—manifestations of architectural decay—and implementation issues. In this paper, we take a step further in exploring this phenomenon. We analyze the available development data from 10 open-source software systems and show that information regarding current architectural decay in these systems can be used to build models that accurately predict future issue-proneness and change-proneness of the systems’ implementations. As a less intuitive result, we also show that, in cases where historical data for a system is unavailable, such data from other, unrelated systems can provide reasonably accurate issue- and change-proneness prediction capabilities. 

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2. 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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3. Distributed block-diagonal approximation methods for regularized empirical risk minimization

   https://doi.org/10.1007/s10994-019-05859-2  

   Lee, Ching-pei; Chang, Kai-Wei (January 2019, Machine Learning)

   In recent years, there is a growing need to train machine learning models on a huge volume of data. Therefore, designing efficient distributed optimization algorithms for empirical risk minimization (ERM) has become an active and challenging research topic. In this paper, we propose a flexible framework for distributed ERM training through solving the dual problem, which provides a unified description and comparison of existing methods. Our approach requires only approximate solutions of the sub-problems involved in the optimization process, and is versatile to be applied on many large-scale machine learning problems including classification, regression, and structured prediction. We show that our framework enjoys global linear convergence for a broad class of non-strongly-convex problems, and some specific choices of the sub-problems can even achieve much faster convergence than existing approaches by a refined analysis. This improved convergence rate is also reflected in the superior empirical performance of our method. 

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4. Architecture-based Software Reliability Incorporating Fault Tolerant Machine Learning

   https://doi.org/10.1109/RAMS48030.2020.9153718  

   Nafreen, Maskura; Bhattacharya, Saikath; Fiondella, Lance (July 2020, Annual Reliability and Maintainability Symposium (RAMS 2020))

   null (Ed.)

   With the increased interest to incorporate machine learning into software and systems, methods to characterize the impact of the reliability of machine learning are needed to ensure the reliability of the software and systems in which these algorithms reside. Towards this end, we build upon the architecture-based approach to software reliability modeling, which represents application reliability in terms of the component reliabilities and the probabilistic transitions between the components. Traditional architecture-based software reliability models consider all components to be deterministic software. We therefore extend this modeling approach to the case, where some components represent learning enabled components. Here, the reliability of a machine learning component is interpreted as the accuracy of its decisions, which is a common measure of classification algorithms. Moreover, we allow these machine learning components to be fault-tolerant in the sense that multiple diverse classifier algorithms are trained to guide decisions and the majority decision taken. We demonstrate the utility of the approach to assess the impact of machine learning on software reliability as well as illustrate the concept of reliability growth in machine learning. Finally, we validate past analytical results for a fault tolerant system composed of correlated components with real machine learning algorithms and data, demonstrating the analytical expression’s ability to accurately estimate the reliability of the fault tolerant machine learning component and subsequently the architecture-based software within which it resides. 

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5. FIDLAR: Forecast-Informed Deep Learning Architecture for Flood Mitigation

   https://doi.org/10.1609/aaai.v39i27.35059  

   Shi, Jimeng; Yin, Zeda; Leon, Arturo; Obeysekera, Jayantha; Narasimhan, Giri (April 2025, Proceedings of the AAAI Conference on Artificial Intelligence)

   In coastal river systems, floods, often during major storms or king tides, severely threaten lives and property. However, hydraulic structures such as dams, gates, pumps, and reservoirs exist in these river systems, and these floods can be mitigated or even prevented by strategically releasing water before extreme weather events. A standard approach used by local water management agencies is the “rule-based” method, which specifies predetermined water prereleases based on historical human experience, but which tends to result in excessive or inadequate water release. Iterative optimization methods that rely on detailed physics-based models for prediction are an alternative approach. Whereas, such methods tend to be computationally intensive, requiring hours or even days to solve the problem optimally. In this paper, we propose a Forecast Informed Deep Learning Architecture, FIDLAR, to achieve rapid and near-optimal flood management with precise water prereleases. FIDLAR seamlessly integrates two neural network modules: one called the Flood Manager, which is responsible for generating water pre-release schedules, and another called the Flood Evaluator, which evaluates those generated schedules. The Evaluator module is pre-trained separately, and its gradient-based feedback is utilized to train the Manager model, ensuring near-optimal water pre-releases. We have conducted experiments with a flood-prone coastal area in South Florida. Results show that FIDLAR is several orders of magnitude faster than currently used physics-based approaches while outperforming baseline methods with improved water pre-release schedules. 

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