Studying and Leveraging User-Provided Logs in Bug Reports for Debugging Assistance

Bug reports provide important information for developers to debug user-reported issues. During the debugging process, developers need to study the bug report and examine user-provided logs to understand the system execution paths that lead to the problem. Prior studies on bug reports also found that such user-provided often contain valuable debugging information to developers. In this thesis, we conduct a tool-assisted study to study user-provided logs in bug reports. Our goal is to study any challenges that developers may encounter when analyzing the logs, and how many additional buggy classes can the logs help to identify. In particular, we study both system-generated logs and exception stack traces. Our approach tries to simulate developers' debugging process by 1) identifying the location in the source code where the logs were generated, 2) re-constructing execution paths by finding the call paths are can be traced back from the logs, and 3) studying the additional buggy classes that the re-constructed execution paths identify. We conduct our study on eight large-scale open-source systems with a total of 1,145 bug reports that contain logs. We find that the execution paths cannot be constructed in 32% of the studied bug reports, since many logs can no longer be found in the source code due to code evolution, and users often provide logs that are generated by third-party frameworks. In the rest of the cases, the re-constructed execution paths can identify 15% additional buggy classes in 41% of the bug reports. Through a comprehensive manual study, we find that the main reasons that the re-constructed execution paths fail to identify additional buggy classes are that reporters often only attach logs that describe the unexpected behavior (e.g., stack traces) without additional logs to illustrate the system execution. In summary, this thesis highlights both the challenges and potentials of using user-provided logs to assist developers with debugging. It also revealed common issues with user-provided logs in bug reports, and provided suggestions for future research

Extending the Reach of Fault Localization to Assist in Automated Debugging

Software debugging is one of the most time-consuming tasks in modern software maintenance. To assist developers with debugging, researchers have proposed fault localization techniques. These techniques aim to automate the process of locating faults in software, which can greatly reduce debugging time and assist developers in understanding the faults. Effective fault localization is also crucial for automated program repair techniques, as it helps identify potential faulty locations for patching. Despite recent efforts to advance fault localization techniques, their effectiveness is still limited. With the increasing complexity of modern software, fault localization may not always provide direct identification of the root causes of faults. Further, there is a lack of studies on their application in modern software development. Most prior studies have evaluated these techniques in traditional software development settings, where only a single snapshot of the system is considered. However, modern software development often involves continuous and fine-grained changes to the system. This dissertation proposes a series of approaches to explore new automated debugging solutions that can enhance software quality assurance and reliability practices, with a specific focus on extending the reach of fault localization in modern software development. The dissertation begins with an empirical study on user-reported logs in bug reports, revealing that re-constructed execution paths from these logs provide valuable debugging hints. To further assist developers in debugging, we propose using static analysis techniques for information-retrieval and path-guided fault localization. By leveraging execution paths from logs in bug reports, we can improve the effectiveness of fault localization techniques. Second, we investigate the characteristics of operational data in continuous integration that can help capture faults early in the testing phase. As there is currently no available continuous integration benchmark that incorporates continuous test execution and failure, we present T-Evos, a dataset that comprises various operational data in continuous integration settings. We propose automated fault localization techniques that integrate change information from continuous integration settings, and demonstrate that leveraging such fine-grained change information can significantly improve their effectiveness. Finally, the dissertation investigates the data cleanness in fault localization by examining developers' knowledge in fault-triggering tests. The study reveals a significant degradation in the performance of fault localization techniques when evaluated on faults without developer knowledge. Through case studies and experiments, the proposed techniques in this dissertation significantly improve the effectiveness of fault localization and facilitate their adoption in modern software development. Additionally, this dissertation provides valuable insights into new debugging solutions for future research

Back to the Future! Studying Data Cleanness in Defects4J and its Impact on Fault Localization

For software testing research, Defects4J stands out as the primary benchmark dataset, offering a controlled environment to study real bugs from prominent open-source systems. However, prior research indicates that Defects4J might include tests added post-bug report, embedding developer knowledge and affecting fault localization efficacy. In this paper, we examine Defects4J's fault-triggering tests, emphasizing the implications of developer knowledge of SBFL techniques. We study the timelines of changes made to these tests concerning bug report creation. Then, we study the effectiveness of SBFL techniques without developer knowledge in the tests. We found that 1) 55% of the fault-triggering tests were newly added to replicate the bug or to test for regression; 2) 22% of the fault-triggering tests were modified after the bug reports were created, containing developer knowledge of the bug; 3) developers often modify the tests to include new assertions or change the test code to reflect the changes in the source code; and 4) the performance of SBFL techniques degrades significantly (up to --415% for Mean First Rank) when evaluated on the bugs without developer knowledge. We provide a dataset of bugs without developer insights, aiding future SBFL evaluations in Defects4J and informing considerations for future bug benchmarks

Are They All Good? Studying Practitioners' Expectations on the Readability of Log Messages

Developers write logging statements to generate logs that provide run-time information for various tasks. The readability of log messages in the logging statements (i.e., the descriptive text) is rather crucial to the value of the generated logs. Immature log messages may slow down or even obstruct the process of log analysis. Despite the importance of log messages, there is still a lack of standards on what constitutes good readability in log messages and how to write them. In this paper, we conduct a series of interviews with 17 industrial practitioners to investigate their expectations on the readability of log messages. Through the interviews, we derive three aspects related to the readability of log messages, including Structure, Information, and Wording, along with several specific practices to improve each aspect. We validate our findings through a series of online questionnaire surveys and receive positive feedback from the participants. We then manually investigate the readability of log messages in large-scale open source systems and find that a large portion (38.1%) of the log messages have inadequate readability. Motivated by such observation, we further explore the potential of automatically classifying the readability of log messages using deep learning and machine learning models. We find that both deep learning and machine learning models can effectively classify the readability of log messages with a balanced accuracy above 80.0% on average. Our study provides comprehensive guidelines for composing log messages to further improve practitioners' logging practices.Comment: Accepted as a research paper at the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023

T cell immunity rather than antibody mediates cross-protection against Zika virus infection conferred by a live attenuated Japanese encephalitis SA14-14-2 vaccine.

Zika virus (ZIKV) and Japanese encephalitis virus (JEV) are closely related to mosquito-borne flaviviruses. Japanese encephalitis (JE) vaccine SA14-14-2 has been in the Chinese national Expanded Program on Immunization since 2007. The recent recognition of severe disease syndromes associated with ZIKV, and the identification of ZIKV from mosquitoes in China, prompts an urgent need to investigate the potential interaction between the two. In this study, we showed that SA14-14-2 is protective against ZIKV infection in mice. JE vaccine SA14-14-2 triggered both Th1 and Th2 cross-reactive immune responses to ZIKV; however, it was cellular immunity that predominantly mediated cross-protection against ZIKV infection. Passive transfer of immune sera did not result in significant cross-protection but did mediate antibody-dependent enhancement in vitro, though this did not have an adverse impact on survival. This study suggests that the SA14-14-2 vaccine can protect against ZIKV through a cross-reactive T cell response. This is vital information in terms of ZIKV prevention or precaution in those ZIKV-affected regions where JEV circulates or SA14-14-2 is in widespread use, and opens a promising avenue to develop a novel bivalent vaccine against both ZIKV and JEV. KEY POINTS: • JEV SA14-14-2 vaccine conferred cross-protection against ZIKV challenge in mice. • T cell immunity rather than antibody mediated the cross-protection. • It provides important information in terms of ZIKV prevention or precaution

Cross-Protection Against Four Serotypes of Dengue Virus in Mice Conferred by a Zika DNA Vaccine

Both Zika virus (ZIKV) and four serotypes of dengue virus (DENV1–4) are antigenically related mosquito-borne flaviviruses that co-circulate in overlapping geographic distributions. The considerable amino acid sequence homology and structural similarities between ZIKV and DENV1–4 may be responsible for the complicated immunological cross-reactivity observed for these viruses. Thus, a successful Zika vaccine needs to not only confer protection from ZIKV infection but must also be safe during secondary exposures with other flavivirus, especially DENVs. In this study, we used a Zika DNA vaccine candidate (pV-ZME) expressing the ZIKV premembrane and envelop proteins to immunize BALB/c mice and evaluated the potential cross-reactive immune responses to DENV1–4. We observed that three doses of the pV-ZME vaccine elicited the production of cross-reactive antibodies, cytokines and CD8+ T cell responses and generated cross-protection against DENV1–4. Our results demonstrate a novel approach for design and development of safe Zika and/or dengue vaccines

Intelligent diagnostic scheme for lung cancer screening with Raman spectra data by tensor network machine learning

Artificial intelligence (AI) has brought tremendous impacts on biomedical sciences from academic researches to clinical applications, such as in biomarkers' detection and diagnosis, optimization of treatment, and identification of new therapeutic targets in drug discovery. However, the contemporary AI technologies, particularly deep machine learning (ML), severely suffer from non-interpretability, which might uncontrollably lead to incorrect predictions. Interpretability is particularly crucial to ML for clinical diagnosis as the consumers must gain necessary sense of security and trust from firm grounds or convincing interpretations. In this work, we propose a tensor-network (TN)-ML method to reliably predict lung cancer patients and their stages via screening Raman spectra data of Volatile organic compounds (VOCs) in exhaled breath, which are generally suitable as biomarkers and are considered to be an ideal way for non-invasive lung cancer screening. The prediction of TN-ML is based on the mutual distances of the breath samples mapped to the quantum Hilbert space. Thanks to the quantum probabilistic interpretation, the certainty of the predictions can be quantitatively characterized. The accuracy of the samples with high certainty is almost 100$\%$. The incorrectly-classified samples exhibit obviously lower certainty, and thus can be decipherably identified as anomalies, which will be handled by human experts to guarantee high reliability. Our work sheds light on shifting the ``AI for biomedical sciences'' from the conventional non-interpretable ML schemes to the interpretable human-ML interactive approaches, for the purpose of high accuracy and reliability.Comment: 10 pages, 7 figure

Vaccination With a Single Consensus Envelope Protein Ectodomain Sequence Administered in a Heterologous Regimen Induces Tetravalent Immune Responses and Protection Against Dengue Viruses in Mice

The development of a safe and effective tetravalent dengue vaccine that elicits protection against all dengue virus (DENV) serotypes is urgently needed. The consensus sequence of the ectodomain of envelope (E) protein of DENV (cE80) has been examined as an immunogen previously. In the current study, a cE80 DNA (D) vaccine was constructed and evaluated in conjunction with the cE80 protein (P) vaccine to examine whether both vaccines used together can further improve the immune responses. The cE80 DNA vaccine was administrated using either a homologous (DNA alone, DDD) or heterologous (DNA prime-protein boost: DDP or DPP) regimen, and evaluated for immunogenicity and protective efficacy in mice. Among the three DNA-based immunization regimens tested, DDP immunization is the optimal immunization regimen that elicited the greatest systemic immune response and conferred protection against all four DENV serotypes. This work provides innovative ideas for the development of consensus E-based dengue vaccines and the testing of optimal immunization regimens

PMEPA1 isoform a drives progression of glioblastoma by promoting protein degradation of the Hippo pathway kinase LATS1

The Hippo signaling pathway controls organ development and is also known, in cancer, to have a tumor suppressing role. Within the Hippo pathway, we here demonstrate, in human gliomas, a functional interaction of a transmembrane protein, prostate transmembrane protein, androgen induced 1 (PMEPA1) with large tumor suppressor kinase 1 (LATS1). We show that PMEPA1 is upregulated in primary human gliomas. The PMEPA1 isoform PMEPA1a was predominantly expressed in glioma specimens and cell lines, and ectopic expression of the protein promoted glioma growth and invasion in vitro and in an orthotopic xenograft model in nude mice. In co-immunoprecipitation experiments, PMEPA1a associated with the Hippo tumor suppressor kinase LATS1. This interaction led to a proteasomal degradation of LATS1 through recruitment of the ubiquitin ligase, neural precursor cell expressed, developmentally downregulated 4 (NEDD4), which led to silencing of Hippo signaling. Alanine substitution in PMEPA1a at PY motifs resulted in failed LATS1 degradation. Targeting of a downstream component in the Hippo signaling pathway, YAP, with shRNA, interfered with the growth promoting activities of PMEPA1a in vitro and in vivo. In conclusion, the presented work shows that PMEPA1a contributes to glioma progression by a dysregulation of the Hippo signaling pathway and thus represents a promising target for the treatment of gliomas.publishedVersio