The advent of Large Language Models (LLMs) has spurred the development of coding agents for real-world code generation.As a widely used benchmark for evaluating the code generation capabilities of these agents, SWE-Bench uses real-world problems based on GitHub issues and their corresponding pull requests.However, the manually written test cases included in these pull requests are often insufficient, allowing generated patches to pass the tests without resolving the underlying issue.To address this challenge, we introduce UTGenerator, an LLM-driven test case generator that automatically analyzes codebases and dependencies to generate test cases for real-world Python projects.Building on UTGenerator, we propose UTBoost, a comprehensive framework for test case augmentation.In our evaluation, we identified 36 task instances with insufficient test cases and uncovered 345 erroneous patches incorrectly labeled as passed in the original SWE Bench.These corrections, impacting 40.9% of SWE-Bench Lite and 24.4% of SWE-Bench Verified leaderboard entries, yield 18 and 11 ranking changes, respectively.

Open-source software (OSS) plays a crucial role in modern software development. Utilizing OSS code can greatly accelerate software development, reduce redundancy, and enhance reliability. Python, a widely adopted programming language, is renowned for its extensive and diverse third-party package ecosystem. However, a significant number of OSS packages within the Python ecosystem are in poor maintenance, leading to potential risks in functionality and security. Consequently, it is essential to establish a deprecation mechanism to assist package developers and users in managing packages effectively. To facilitate the establishment of the package-level deprecation mechanism, this paper presents a mixed-method empirical study, including data analysis and surveys. We investigate the current practices of announcing, receiving, and handling package-level deprecation in the Python ecosystem. We also assess the benefits of having deprecation announcements for inactively maintained packages. Furthermore, we investigate the challenges faced by package developers and users and their expectations for future deprecation practices. Our findings reveal that 75.4% of inactive package developers have no intention of releasing deprecation declarations for various reasons, while 89.5% of users express a desire to be notified about the deprecation, highlighting a gap between developers and users; in many cases, no alternative solutions are available when deprecation occurs, emphasizing the need to explore practical approaches that enable seamless package handover and require less maintenance effort. Our work aims to enhance the understanding of existing package-level deprecation patterns within the Python OSS realm and facilitate the development of deprecation practices for the Python community in the future.

We consider the role of non-localities in speed-density data used to fit fundamental diagrams from vehicle trajectories. We demonstrate that the use of anticipated densities results in a clear classification of speed-density data into stationary and non-stationary points, namely, acceleration and deceleration regimes and their separating boundary. The separating boundary represents a locus of stationary traffic states, i.e., the fundamental diagram. To fit fundamental diagrams, we develop an enhanced cross entropy minimization method that honors equilibrium traffic physics. We illustrate the effectiveness of our proposed approach by comparing it with the traditional approach that uses local speed-density states and least squares estimation. Our experiments show that the separating boundary in our approach is invariant to varying trajectory samples within the same spatio-temporal region, providing further evidence that the separating boundary is indeed a locus of stationary traffic states.

Graph Database Management Systems (GDBMS), which utilize graph models for data storage and execute queries via graph traversals, have seen ubiquitous usage in real-world scenarios such as recommendation systems, knowledge graphs, and social networks. Much like Relational Database Management Systems (RDBMS), GDBMS are not immune to bugs. These bugs typically manifest as logic errors that yield incorrect results (e.g., omitting a node that should be included), performance bugs (e.g., long execution time caused by redundant graph scanning), and exception issues (e.g., unexpected or missing exceptions).

While deep learning (DL) has emerged as a powerful technique, its benefits must be carefully considered in relation to computational costs. Specifically, although DL methods have achieved strong performance in log anomaly detection, they often require extended time for log preprocessing, model training, and model inference, hindering their adoption in online distributed cloud systems that require rapid deployment of log anomaly detection service.

Image captioning (IC) systems aim to generate a text description of the salient objects in an image. In recent years, IC systems have been increasingly integrated into our daily lives, such as assistance for visually-impaired people and description generation in Microsoft Powerpoint. However, even the cutting-edge IC systems (e.g., Microsoft Azure Cognitive Services) and algorithms (e.g., OFA) could produce erroneous captions, leading to incorrect captioning of important objects, misunderstanding, and threats to personal safety. The existing testing approaches either fail to handle the complex form of IC system output (i.e., sentences in natural language) or generate unnatural images as test cases. To address these problems, we introduce Recursive Object MElting (ROME), a novel metamorphic testing approach for validating IC systems. Different from existing approaches that generate test cases by inserting objects, which easily make the generated images unnatural, ROME melts (i.e., remove and inpaint) objects. ROME assumes that the object set in the caption of an image includes the object set in the caption of a generated image after object melting. Given an image, ROME can recursively remove its objects to generate different pairs of images. We use ROME to test one widely-adopted image captioning API and four state-of-the-art (SOTA) algorithms. The results show that the test cases generated by ROME look much more natural than the SOTA IC testing approach and they achieve comparable naturalness to the original images. Meanwhile, by generating test pairs using 226 seed images, ROME reports a total of 9,121 erroneous issues with high precision (86.47%-92.17%). In addition, we further utilize the test cases generated by ROME to retrain the Oscar, which improves its performance across multiple evaluation metrics.

Named entity recognition (NER) systems have seen rapid progress in recent years due to the development of deep neural networks. These systems are widely used in various natural language processing applications, such as information extraction, question answering, and sentiment analysis. However, the complexity and intractability of deep neural networks can make NER systems unreliable in certain circumstances, resulting in incorrect predictions. For example, NER systems may misidentify female names as chemicals or fail to recognize the names of minority groups, leading to user dissatisfaction. To tackle this problem, we introduce TIN, a novel, widely applicable approach for automatically testing and repairing various NER systems. The key idea for automated testing is that the NER predictions of the same named entities under similar contexts should be identical. The core idea for automated repairing is that similar named entities should have the same NER prediction under the same context. We use TIN to test two SOTA NER models and two commercial NER APIs, i.e., Azure NER and AWS NER. We manually verify 784 of the suspicious issues reported by TIN and find that 702 are erroneous issues, leading to high precision (85.0%-93.4%) across four categories of NER errors: omission, over-labeling, incorrect category, and range error. For automated repairing, TIN achieves a high error reduction rate (26.8%-50.6%) over the four systems under test, which successfully repairs 1,056 out of the 1,877 reported NER errors.

Image captioning (IC) systems, which automatically generate a text description of the salient objects in an image (real or synthetic), have seen great progress over the past few years due to the development of deep neural networks. IC plays an indispensable role in human society, for example, labeling massive photos for scientific studies and assisting visually-impaired people in perceiving the world. However, even the top-notch IC systems, such as Microsoft Azure Cognitive Services and IBM Image Caption Generator, may return incorrect results, leading to the omission of important objects, deep misunderstanding, and threats to personal safety.