The superior functionality and versatility of generative AI have raised expectations for the improvement of human society and concerns about the ethical and social risks associated with the use of generative AI. Many previous studies have presented risk issues as concerns associated with the use of generative AI, but since most of these concerns are from the user's perspective, they are difficult to lead to specific countermeasures. In this study, the risk issues presented by the previous studies were broken down into more detailed elements, and risk factors and impacts were identified. In this way, we presented information that leads to countermeasure proposals for generative AI risks.

Machine learning system analysis requires different approaches for each different task and domain. Selecting a proper set of analytic models can be challenging for a specific problem. This paper discusses the extensibility of the Multi-View Modeling Framework for ML Systems approach using process mapping and extensible metamodel. We conducted a case study to evaluate the feasibility of such extensibility by extending the approach to facilitate an activity-driven analysis for an optical character recognition system. Based on the result of the case study, we found that Multi-View Modeling Framework for ML Systems is likely to be extensible.

In this paper, we propose the first verification method for attention robustness, i.e., the local robustness of the changes in the saliency-map against combinations of semantic perturbations. Specmcally, our method determines the range of the perturbation parameters (e.g., the amount of brightness change) that maintains the difference between the actual saliencymap change and the expected saliency-map change below a given threshold value. Our method is based on linear activation region traversals, focusing on the outermost boundary of attention robustness for scalability on larger deep neural networks.

Systematic techniques to improve quality of deep neural networks (DNNs) are critical given the increasing demand for practical applications including safety-critical ones. The key challenge comes from the little controllability in updating DNNs. Retraining to fix some behavior often has a destructive impact on other behavior, causing regressions, i.e., the updated DNN fails with inputs correctly handled by the original one. This problem is crucial when engineers are required to investigate failures in intensive assurance activities for safety or trust. Search-based repair techniques for DNNs have potentials to tackle this challenge by enabling localized updates only on “responsible parameters” inside the DNN. However, the potentials have not been explored to realize sufficient controllability to suppress regressions in DNN repair tasks. In this paper, we propose a novel DNN repair method that makes use of the training history for judging which DNN parameters should be changed or not to suppress regressions. We implemented the method into a tool called Neurecover and evaluated it with three datasets. Our method outperformed the existing method by achieving often less than a quarter, even a tenth in some cases, number of regressions. Our method is especially effective when the repair requirements are tight to fix specific failure types. In such cases, our method showed stably low rates (<2 %) of regressions, which were in many cases a tenth of regressions caused by retraining.

Application Programming Interfaces (APIs) are common in software development to reuse other products. Although the documentation allows API consumers to learn about API usages, it can be unreliable. Here, we investigate the characteristics of inappropriate usage examples in web API documentation by extracting and comparing OpenAPI Specifications from usage example-response pairs. About 65.5% of the endpoints have some form of inappropriate usage examples. Furthermore, mismatches are classified into four categories: undocumented keys pattern, dynamic keys pattern, unreturned keys pattern, and type mismatched pattern. Our results suggest that the number of keys in the response is correlated with the number of mismatches. These findings should assist both API providers and consumers who deal with unreliable documentation in web APIs.

Symbolic execution as a test case generation technique has recently become an active research area. However, since symbolic execution generates a large number of test cases, it is impractical to run all the generated test cases in practice. In this paper, we present a test case selection method for a symbolic execution-based test case generation. This method has the following two characteristics. 1)Test cases which cover atomic conditions collected during symbolic execution, called path condition-based, are selected to keep the fault-detection capability. 2)This method does not depend on a particular symbolic execution engine since it is based on an analysis of path conditions in a general format. We implemented this method in our tool and evaluated this method with real systems. Our evaluation shows that the method produces a significant reduction in the size of the test suite while effectively preserving its fault-detection capability. We also demonstrate that the method is compatible with practically any symbolic execution engine, including the popular tools Java Path Finder and KLEE.

After a legacy system is re-engineered, it is important to perform compatibility testing so as to identify the difference and reduce the introduced bugs. We can first apply symbolic execution to obtain an exhaustive set of test cases, then use them to check the compatibility of the old system and the new old. However, the path explosion problem of symbolic execution makes it difficult to work on realistic non-trivial applications. We show in this paper how to enhance symbolic execution, e.g. with extra constraints, path cutting, variable grouping, and test case selection, to successfully test the compatibility of an SMTP library (used in embedded systems) with around 20K lines of code. Our experience indicates that these enhancements are essential to apply symbolic execution on realistic industrial applications.