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As autonomous driving systems (ADSes) become increasingly complex and integral to daily life, the importance of understanding the nature and mitigation of software bugs in these systems has grown correspondingly. Addressing the challenges of software maintenance in autonomous driving systems (e.g., handling real-time system decisions and ensuring safety-critical reliability) is crucial due to the unique combination of real-time decision-making requirements and the high stakes of operational failures in ADSes. The potential of automated tools in this domain is promising, yet there remains a gap in our comprehension of the challenges faced and the strategies employed during manual debugging and repair of such systems. In this paper, we present an empirical study that investigates bug-fix patterns in ADSes, with the aim of improving reliability and safety. We have analyzed the commit histories and bug reports of two major autonomous driving projects, Apollo and Autoware, from 1,331 bug fixes with the study of bug symptoms, root causes, and bug-fix patterns. Our study reveals several dominant bug-fix patterns, including those related to path planning, data flow, and configuration management. Additionally, we find that the frequency distribution of bug-fix patterns varies significantly depending on their nature and types and that certain categories of bugs are recurrent and more challenging to exterminate. Based on our findings, we propose a hierarchy of ADS bugs and two taxonomies of 15 syntactic bug-fix patterns and 27 semantic bug-fix patterns that offer guidance for bug identification and resolution. We also contribute a benchmark of 1,331 ADS bug-fix instances. 

Salinity stress threatens soil microbiomes, a key driver of soil multifunctionality and health. This study employed high-throughput sequencing of 16S rRNA, PLFAs, multifunctionality index, and co-occurrence networks to gain a comprehensive understanding of the dynamic responses of soil microbiomes to salinity stress gradient (0, 0.4 and 1 mol NaCl). Additionally, we investigated how these responses are shaped by the addition of vermicompost and NPK fertilizer during short-term (2-h) and long-term (70-day) incubation periods. Salinity stress reduced bacterial and fungal phospholipid fatty acids (PLFA) concentrations in the short-term. Over the long-term, the microbial community evolved into a new pattern under salt stress, favoring the presence ofBacteriodota, a salt-tolerant phylum, while decreasing the relative abundance ofAcidobacteriotaandPlanctomycetota, which are more salt-sensitive. Furthermore, salinity decreased species richness by 11.33% and soil multifunctionality by 21.48% but increased microbial network complexity while decreasing its stability. Incorporating vermicompost increased bacterial and fungal PLFAs, enhanced bacterial diversity by 2.33%, promoted salt-tolerant bacteria, and increased the complexity and stability of the bacterial network. Conversely, the application of NPK fertilizer reduced bacterial richness, alpha diversity and soil multifunctionality by 14.52, 5.83, and 12.34%, respectively, further disrupting the microbial community and making resilience to salinity stress more challenging. Furthermore, NPK fertilization increased bacterial network complexity but decreased its stability. This study underscores the significance of employing vermicompost to improve the health of saline soils. Furthermore, it emphasizes the negative impacts of NPK fertilizer on soil microbial structure and function and hinder its recovery from salinity’s impacts. 

The optimization of a system’s configuration options is crucial for determining its performance and functionality, particularly in the case of autonomous driving software (ADS) systems because they possess a multitude of such options. Research efforts in the domain of ADS have prioritized the development of automated testing methods to enhance the safety and security of self-driving cars. Presently, search-based approaches are utilized to test ADS systems in a virtual environment, thereby simulating real-world scenarios. However, such approaches rely on optimizing the waypoints of ego cars and obstacles to generate diverse scenarios that trigger violations, and no prior techniques focus on optimizing the ADS from the perspective of configuration. To address this challenge, we present a framework called ConfVE, which is the first automated configuration testing framework for ADSes. ConfVE’s design focuses on the emergence of violations through rerunning scenarios generated by different ADS testing approaches under different configurations, leveraging 9 test oracles to enable previous ADS testing approaches to find more types of violations without modifying their designs or implementations and employing a novel technique to identify bug-revealing violations and eliminate duplicate violations. Our evaluation results demonstrate that ConfVE can discover 1,818 unique violations and reduce 74.19% of duplicate violations. 

Scholars have investigated numerous barriers to accessible software development tools and processes for Blind and Low Vision (BLV) developers. However, the research community has yet to study the accessibility of software development meetings, which are known to play a crucial role in software development practice. We conducted semi-structured interviews with 26 BLV software professionals about software development meeting accessibility. We found four key themes related to in-person and remote software development meetings: (1) participants observed that certain meeting activities and software tools used in meetings were inaccessible, (2) participants performed additional labor in order to make meetings accessible, (3) participants avoided disclosing their disability during meetings due to fear of career repercussions, (4) participants suggested technical, social and organizational solutions for accessible meetings, including developing their own solutions. We suggest recommendations and design implications for future accessible software development meetings including technical and policy-driven solutions. 

Android is a highly fragmented platform with a diverse set of devices and users. To support the deployment of apps in such a heterogeneous setting, Android has introduceddynamic delivery—a new model of software deployment in which optional, device- or user-specific functionalities of an app, calledDynamic Feature Modules (DFMs), can be installed, as needed, after the app’s initial installation. This model of app deployment, however, has exacerbated the challenges of properly testing Android apps. In this article, we first describe the results of an extensive study in which we formalized a defect model representing the various conditions under which DFM installations may fail. We then presentDeltaDroid—a tool aimed at assisting the developers with validating dynamic delivery behavior in their apps by augmenting their existing test suite. Our experimental evaluation using real-world apps corroboratesDeltaDroid’s ability to detect many crashes and unexpected behaviors that the existing automated testing tools cannot reveal.