Defects4J stands out as a leading benchmark dataset for software testing research, providing a controlled environment to study real bugs from prominent open-source systems. While Defects4J provides a clean and valuable dataset, we aim to explore how fault localization techniques perform under less-controlled development scenarios. In this paper, we revisited Defects4J to study developers’ changes to fault-triggering tests after the bugs were reported/fixed. We aim to introduce a new evaluation scenario within Defects4J, focusing on the implications of regression tests and test changes added after the bug was fixed. We analyze when these tests were modified relative to bug report creation and examine spectrum-based fault localization (SBFL) performance in less-controlled settings. Our findings show that 1) 55% of the fault-triggering tests were added to replicate the bug or test for regression; 2) 22% of the tests were changed after the bug reports, incorporating information related to the bug; 3) developers often update tests with new assertions or changes to match source code updates; and 4) SBFL performance differs significantly in less-controlled settings (down by at most 90% for Mean First Rank). Our study points out the diverse development scenarios in the studied bugs, highlighting new settings for future SBFL evaluations and bug benchmarks.

We constructed a newly large-scale and comprehensive C/C++ vulnerability dataset named MegaVul by crawling the Common Vulnerabilities and Exposures (CVE) database and CVE-related open-source projects. Specifically, we collected all crawlable descriptive information of the vulnerabilities from the CVE database and extracted all vulnerability-related code changes from 28 Git-based websites. We adopt advanced tools to ensure the extracted code integrality and enrich the code with four different transformed representations. Totally, MegaVul contains 17,380 vulnerabilities collected from 992 open-source repositories spanning 169 different vulnerability types disclosed from January 2006 to October 2023. Thus, MegaVul can be used for a variety of software security-related tasks including detecting vulnerabilities and assessing vulnerability severity. All information is stored in the JSON format for easy usage. MegaVul is publicly available on GitHub and will be continuously updated. It can be easily extended to other programming languages.

An organized knowledge base can facilitate the exploration of existing knowledge and the detection of emerging topics in a domain. Knowledge about and around Geographic Information Science and its associated system technologies (GIS) is complex, extensive and emerging rapidly. Taking the challenge, we built a GIS knowledge graph (GIS-KG) by (1) merging existing GIS bodies of knowledge to create a hierarchical ontology and then (2) applying deep-learning methods to map GIS publications to the ontology. We conducted several experiments on information retrieval to evaluate the novelty and effectiveness of the GIS-KG. Results showed the robust support of GIS-KG for knowledge search of existing GIS topics and potential to explore emerging research themes.

Street view imagery such as Google Street View is widely used in people’s daily lives. Many studies have been conducted to detect and map objects such as traffic signs and sidewalks for urban built-up environment analysis. While mapping objects in the horizontal dimension is common in those studies, automatic vertical measuring in large areas is underexploited. Vertical information from street view imagery can benefit a variety of studies. One notable application is estimating the lowest floor elevation, which is critical for building flood vulnerability assessment and insurance premium calculation. In this article, we explored the vertical measurement in street view imagery using the principle of tacheometric surveying. In the case study of lowest floor elevation estimation using Google Street View images, we trained a neural network (YOLO-v5) for door detection and used the fixed height of doors to measure doors’ elevation. The results suggest that the average error of estimated elevation is 0.218 m. The depthmaps of Google Street View were utilized to traverse the elevation from the roadway surface to target objects. The proposed pipeline provides a novel approach for automatic elevation estimation from street view imagery and is expected to benefit future terrain-related studies for large areas.

We collected a large C/C++ code vulnerability dataset from open-source Github projects, namely Big-Vul. We crawled the public Common Vulnerabilities and Exposures (CVE) database and CVE-related source code repositories. Specifically, we collected the descriptive information of the vulnerabilities from the CVE database, e.g., CVE IDs, CVE severity scores, and CVE summaries. With the CVE information and its related published Github code repository links, we downloaded all of the code repositories and extracted vulnerability related code changes. In total, Big-Vul contains 3,754 code vulnerabilities spanning 91 different vulnerability types. All these code vulnerabilities are extracted from 348 Github projects. All information is stored in the CSV format. We linked the code changes with the CVE descriptive information. Thus, our Big-Vul can be used for various research topics, e.g., detecting and fixing vulnerabilities, analyzing the vulnerability related code changes. Big-Vul is publicly available on Github.

The success of question and answer (Q&A) websites attracts massive user-generated content for using and learning APIs, which easily leads to information overload: many questions for APIs have a large number of answers containing useful and irrelevant information, and cannot all be consumed by developers. In this work, we develop DeepTip, a novel deep learning-based approach using different Convolutional Neural Network architectures, to extract short practical and useful tips from developer answers. Our extensive empirical experiments prove that DeepTip can extract useful tips from a large corpus of answers to questions with high precision (i.e., avg. 0.854) and coverage (i.e., 0.94), and it outperforms two state-of-the-art baselines by up to 56.7% and 162%, respectively, in terms of Precision. Furthermore, qualitatively, a user study is conducted with real Stack Overflow users and its results confirm that tip extraction is useful and our approach generates high-quality tips.

Nowadays, many software organizations rely on automatic problem reporting tools to collect crash reports directly from users' environments. These crash reports are later grouped together into crash types. Usually, developers prioritize crash types based on the number of crash reports and file bugs for the top crash types. Because a bug can trigger a crash in different usage scenarios, different crash types are sometimes related to a same bug. Two bugs are correlated when the occurrence of one bug causes the other bug to occur. We refer to a group of crash types related to identical or correlated bugs, as a crash correlation group. In this paper, we propose three rules to identify correlated crash types automatically. We also propose an algorithm to locate and rank buggy files using crash correlation groups. Through an empirical study on Firefox and Eclipse, we show that the three rules can identify crash correlation groups with a precision of 100% and a recall of 90% for Firefox and a precision of 79% and a recall of 65% for Eclipse. On the top three buggy file candidates, the proposed bug localization algorithm achieves a recall of 62% and a precision of 42% for Firefox and a recall of 52% and a precision of 50% for Eclipse. On the top 10 buggy file candidates, the recall increases to 92% for Firefox and 90% for Eclipse. Developers can combine the proposed crash correlation rules with the new bug localization algorithm to identify and fix correlated crash types all together.