Different from mobile ad hoc networks (MANETs) and vehicular ad hoc networks (VANETs), a flying ad hoc network (FANET) is a very low-density network where node topology changes rapidly and irregularly. These characteristics, the density, mobility, and speed of flight nodes, affect the performance of FANET. Furthermore, application scenarios and environmental settings could affect the performance of FANETs. In this paper, we analyzed the representative FANET protocols, AODV, DSDV, and OLSR, according to mobility models, SRWP, MP, RDPZ, EGM, and DPR, under the multi-UAV-based reconnaissance scenario. We evaluated them in terms of the number of nodes, network connectivity, mobility model’s reconnaissance rate, speed of nodes, and ground control station (GCS) location. As a result, we found that AODV showed the highest PDR performance (81%) with SRWP in multiple UAV-based reconnaissance scenarios. As for a mobility model under the consideration of reconnaissance rate, SRWP was excellent at 76%, and RDPZ and EGM mobility models were reasonable at 62% and 60%, respectively. We also made several interesting observations such as how when the number of nodes increases, the connectivity of the network increases, but the performance of the routing protocol decreases, and how the GCS location affects the PDR performance of the combination of routing protocols and mobility models.

Application programming interfaces (APIs) continually evolve to meet ever-changing user needs, and documentation provides an authoritative reference for their usage. However, API documentation is commonly outdated because nearly all of the associated updates are performed manually. Such outdated documentation, especially with regard to API names, causes major software development issues. In this paper, we propose a method for automatically updating outdated API names in API documentation. Our insight is that API updates in documentation can be derived from API implementation changes between code revisions. To evaluate the proposed method, we applied it to four open source projects. Our evaluation results show that our method, FreshDoc, detects outdated API names in API documentation with 48% higher accuracy than the existing state-of-the-art methods do. Moreover, when we checked the updates suggested by FreshDoc against the developers? manual updates in the revised documentation, FreshDoc addressed 82% of the outdated names. When we reported 40 outdated API names found by FreshDoc via issue tracking systems, developers accepted 75% of the suggestions. These evaluation results indicate that FreshDoc can be used as a practical method for the detection and updating of API names in the associated documentation.

Issue reports are software artifacts that often specify the changed requirements of software systems. As software systems evolve according to these changed requirements, issue reports have become the essential artifacts that should be covered by requirement traceability. While researchers have developed automatic approaches for establishing the traceability links of issue reports, no papers have surveyed these approaches. In this paper, we conduct a systematic literature review of issue-based requirement traceability. We searched for articles published in renowned conferences and journals in the software engineering field from 2011 to 2022. From 1,347 initial articles, we identified 40 relevant articles. We investigated four aspects of issue-based traceability: problems, artifact pairs, techniques, and evaluation targets. Our findings are as follows. First, the challenges of issue-based requirement traceability are relevant to accuracy, effort, support, information, and trustworthiness. Second, issue reports are linked to commits, source code, user reviews, and test cases. Third, the studies mainly adopted machine learning and information retrieval techniques to generate and recover trace links. Finally, the main evaluation targets were open-source projects, but open datasets were also provided.

On GitHub, one of the most successful services for software project hosting, labels have been used to represent various information and decisions about reported issues. However, previous studies on labels were limited to simple statistics or label recommendations for issue types. In this paper, we aim to provide a better understanding of labels and their usage in software development. We particularly focus on using multiple and custom labels on issues. To analyze label usage, we collected software project data and label usage information from GitHub. We then quantitatively investigated the performance of projects with multi-label features and qualitatively investigated the categories of multi-labels, and the usage of multi-labels based on these categories. Our analysis results show that multi-labels are common in the majority of software projects and that projects using multi-label features manage their issues more effectively. In addition, our analysis results reveal different types of information represented by labels, which are related to features, development, and issues. This study finds several facts that can be used for studies on issue management and thus that help develop labeling techniques to mitigate the burden of issue management.

Issue reports are valuable resources for the continuous maintenance and improvement of software. Managing issue reports requires a significant effort from developers. To address this problem, many researchers have proposed automated techniques for classifying issue reports. However, those techniques fall short of yielding reasonable classification accuracy. We notice that those techniques rely on text-based unimodal models. In this paper, we propose a novel multimodal model-based classification technique to use heterogeneous information in issue reports for issue classification. The proposed technique combines information from text, images, and code of issue reports. To evaluate the proposed technique, we conduct experiments with four different projects. The experiments compare the performance of the proposed technique with text-based unimodal models. Our experimental results show that the proposed technique achieves a 5.07% to 14.12% higher F1-score than the text-based unimodal models. Our findings demonstrate that utilizing heterogeneous data of issue reports helps improve the performance of issue classification.

Recommendation systems are intended to increase developer productivity by recommending files to edit. These systems mine association rules in software revision histories. However, mining coarse-grained rules using only edit histories produces recommendations with low accuracy, and can only produce recommendations after a developer edits a file. In this work, we explore the use of finer-grained association rules, based on the insight that view histories help characterize the contexts of files to edit. To leverage this additional context and fine-grained association rules, we have developed MI, a recommendation system extending ROSE, an existing edit-based recommendation system. We then conducted a comparative simulation of ROSE and MI using the interaction histories stored in the Eclipse Bugzilla system. The simulation demonstrates that MI predicts the files to edit with significantly higher recommendation accuracy than ROSE (about 63 over 35 percent), and makes recommendations earlier, often before developers begin editing. Our results clearly demonstrate the value of considering both views and edits in systems to recommend files to edit, and results in more accurate, earlier, and more flexible recommendations.