Search Results (16)

Open Source Software (OSS) development is a complex socio-technical system in which collaborator attitudes influence the outcomes. This study empirically analyzes the impact of participant sentiment (positive, neutral, negative) on productivity, defined by Pull Requests (PR), Lines of Code (LoC), and interactions (as indicated by comment volume). Data on PRs, LoC, and comments, were collected from 20 top GitHub repositories. SentiStrength-SE was used to classify participant sentiment based on average comment sentiment. Appropriate nonparametric statistical and correlation analyses were performed. The results showed that contributors with positive sentiments have the highest productivity and interaction. Negative-sentiment contributors also significantly outperform the neutral group in both areas. The neutral group consistently ranks the lowest. The general patterns are as follows: positive > negative > neutral. The strongest positive correlations between productivity and interaction are observed in the positive-sentiment group. These findings empirically demonstrate that the sentiment levels of collaborators are significantly associated with OSS productivity and engagement, offering insights into socio-technical dynamics. Fostering a positive environment is a key strategy for enhancing OSS performance and sustainability. Full article

Open-source software (OSS) projects rely on collaborative contributions, yet sustaining long-term engagement remains a challenge. This study examines how contributor activity frequency and network centrality impact sustained contributions in 672 GitHub repositories. Using k-core decomposition and model checking, we find that contributors with higher k-core values manage more pull requests. Additionally, they show greater resilience in continuing to contribute after pull request rejections, a behavior rarely seen in non-core contributors. Moreover, the likelihood of long-term engagement increases significantly when contributors’ pull requests are processed swiftly, with delayed or rejected pull requests resulting in higher disengagement, particularly among peripheral contributors. We also observe that as project networks grow more complex over time, core contributors become essential in maintaining project sustainability. These findings offer new insights into fostering core contributor retention and underscore the need for efficient governance and PR management practices in OSS projects. Full article

Code reviews are an important part of the software development process, and there is a wide variety of approaches used to perform them. While it is generally agreed that code reviews are beneficial and result in higher-quality software, there has been little work investigating best practices and approaches, exploring which factors impact code review quality. Our approach firstly analyses current best practices and procedures for undertaking code reviews, along with an examination of metrics often used to analyse a review’s quality and current offerings for automated code review assessment. A maximum of one thousand code review comments per project were mined from GitHub pull requests across seven open-source projects which have previously been analysed in similar studies. Several identified metrics are tested across these projects using Python’s Natural Language Toolkit, including stop word ratio, overall sentiment, and detection of code snippets through the GitHub markdown language. Comparisons are drawn with regards to each project’s culture and the language used in the code review process, with pros and cons for each. The results show that the stop word ratio remained consistent across all projects, with only one project exceeding an average of 30%, and that the percentage of positive comments across the projects was broadly similar also. The suitability of these metrics is also discussed with regards to the creation of a scoring framework and development of an automated code review analysis tool. We conclude that the software written is an effective method of comparing practices and cultures across projects and can provide benefits by promoting a positive review culture within an organisation. However, rudimentary sentiment analysis and detection of GitHub code snippets may not be sufficient to assess a code review’s overall usefulness, as many terms that are important to include in a programmer’s lexicon such as ‘error’ and ‘fail’ deem a code review to be negative. Code snippets that are included outside of the markdown language are also ignored from analysis. Recommendations for future work are suggested, including the development of a more robust sentiment analysis system that can include detection of emotion such as frustration, and the creation of a programming dictionary to exclude programming terms from sentiment analysis. Full article

Laparoscopic surgeons are at high risk of experiencing musculoskeletal discomfort, which is considered the result of long-lasting static and awkward body postures. We primarily aimed to evaluate whether passive and active work breaks can reduce ratings of perceived discomfort among laparoscopic surgeons compared with no work breaks. We secondarily aimed to examine potential differences in performance and workload across work break conditions and requested the surgeons evaluate working with passive or active work breaks. Following a balanced, randomized cross-over design, laparoscopic surgeons performed three 90 min laparoscopic simulations without and with 2.5 min passive or active work breaks after 30 min work blocks on separate days. The simulation included the following tasks: a hot wire, peg transfer, pick-and-place, pick-and-tighten, pick-and-thread, and pull-and-stick tasks. Ratings of perceived discomfort (CR10 Borg Scale), performance per subtask, and perceived workload (NASA-TLX) were recorded, and the break interventions were evaluated (self-developed questionnaire). Statistical analyses were performed on the rating of perceived discomfort and a selection of the performance outcomes. Twenty-one participants (9F) were included, with a mean age of 36.6 years (SD 9.7) and an average experience in laparoscopies of 8.5 years (SD 5.6). Ratings of perceived musculoskeletal discomfort slightly increased over time from a mean level of 0.1 to 0.9 but did not statistically significantly differ between conditions (p = 0.439). Performance outcomes of the hot wire and peg transfer tasks did not statistically significantly differ between conditions. The overall evaluation by the participants was slightly in favor regarding the duration and content of active breaks and showed a 65% likelihood of implementing them on their own initiative in ≥90 min-lasting laparoscopic surgeries, compared with passive breaks. Both passive and active breaks did not statistically significantly influence ratings of perceived discomfort or perceived workload in a 90 min simulation of laparoscopic surgery, with an overall low mean level of perceived discomfort of 0.9 (SD 1.4). As work breaks do not lead to performance losses, rest breaks should be tested in real-life situations across a complete working shift, where perceived discomfort may differ from this laboratory situation. However, in this respect, it is crucial to investigate the acceptance and practicality of intraoperative work breaks in feasibility studies in advance of assessing their effectiveness in follow-up longitudinal trials. Full article

Research suggests that feedback in Emergency Medical Services (EMS) positively affects quality of care and professional development. However, the mechanisms by which feedback achieves its effects still need to be better understood across healthcare settings. This study aimed to understand how United Kingdom (UK) ambulance services provide feedback for EMS professionals and develop a programme theory of how feedback works within EMS, using a mixed-methods, realist evaluation framework. A national cross-sectional survey was conducted to identify feedback initiatives in UK ambulance services, followed by four in-depth case studies involving qualitative interviews and documentary analysis. We used qualitative content analysis and descriptive statistics to analyse survey responses from 40 prehospital feedback initiatives, alongside retroductive analysis of 17 interviews and six documents from case study sites. Feedback initiatives mainly provided individual patient outcome feedback through “pull” initiatives triggered by staff requests. Challenges related to information governance were identified. Our programme theory of feedback to EMS professionals encompassed context (healthcare professional and organisational characteristics), mechanisms (feedback and implementation characteristics, psychological reasoning) and outcomes (implementation, staff and service outcomes). This study suggests that most UK ambulance services use a range of feedback initiatives and provides 24 empirically based testable hypotheses for future research. Full article

Named data networking (NDN) is gaining momentum in vehicular ad hoc networks (VANETs) thanks to its robust network architecture. However, vehicular NDN (VNDN) faces numerous challenges, including security, privacy, routing, and caching. Specifically, the attackers can jeopardize vehicles’ cache memory with a Content Poisoning Attack (CPA). The CPA is the most difficult to identify because the attacker disseminates malicious content with a valid name. In addition, NDN employs request–response-based content dissemination, which is inefficient in supporting push-based content forwarding in VANET. Meanwhile, VNDN lacks a secure reputation management system. To this end, our contribution is three-fold. We initially propose a threshold-based content caching mechanism for CPA detection and prevention. This mechanism allows or rejects host vehicles to serve content based on their reputation. Secondly, we incorporate a blockchain system that ensures the privacy of every vehicle at roadside units (RSUs). Finally, we extend the scope of NDN from pull-based content retrieval to push-based content dissemination. The experimental evaluation results reveal that our proposed CPA detection mechanism achieves a 100% accuracy in identifying and preventing attackers. The attacker vehicles achieved a 0% cache hit ratio in our proposed mechanism. On the other hand, our blockchain results identified tempered blocks with 100% accuracy and prevented them from storing in the blockchain network. Thus, our proposed solution can identify and prevent CPA with 100% accuracy and effectively filters out tempered blocks. Our proposed research contribution enables the vehicles to store and serve trusted content in VNDN. Full article

Recently, open-source repositories have grown rapidly due to volunteer contributions worldwide. Collaboration software platforms have gained popularity as thousands of external contributors have contributed to open-source repositories. Although data de-duplication decreases the size of backup workloads, this causes poor data locality (fragmentation) and redundant review time and effort. Deep learning and machine learning techniques have recently been applied to identify complex bugs and duplicate issue reports. It is difficult to use, but it increases the risk of developers submitting duplicate pull requests, resulting in additional maintenance costs. We propose a hybrid deep learning technique in this work on the basis of an optimal duplication detection is for pull requests (HDL-ODPRs) in open-source repositories. An algorithm used to extract textual data from pull requests is hybrid leader-based optimization (HLBO), which increases the accuracy of duplicate detection. Following that, we compute the similarities between pull requests by utilizing the multiobjective alpine skiing optimization (MASO) algorithm, which provides textual, file-change, and code-change similarities. For pull request duplicate detection, a hybrid deep learning technique (named GAN-GS) is introduced, in which the global search (GS) algorithm is used to optimize the design metrics of the generative adversarial network (GAN). The proposed HDL-ODPR model is validated against the public standard benchmark datasets, such as DupPR-basic and DupPR-complementary data. According to the simulation results, the proposed HDL-ODPR model can achieve promising results in comparison with existing state-of-the-art models. Full article

Vehicular Named Data Networking (VNDN) is one of the potential and future networking architectures that allow Connected and Autonomous Vehicles (CAV) to exchange data by simply disseminating the content over the network. VNDN only supports a pull-based data forwarding model, where the content information is forwarded upon request. However, in critical situations, it is essential to design a push-based data forwarding model in order to broadcast the critical data packets without any requests. One of the challenges of push-based data forwarding in VNDN is the broadcasting effect, which occurs when every vehicle broadcasts critical information over the network. For instance, in emergency situations such as accidents, road hazards, and bad weather conditions, the producer generates a critical data packet and broadcasts it to all the nearby vehicles. Subsequently, all vehicles broadcast the same critical data packet to each other, which leads to a broadcast storm on the network. Therefore, this paper proposes a Fuzzy Logic-based Push Data Forwarding (FLPDF) scheme to mitigate the broadcast storm effect. The novelty of this paper is the suggestion and application of a fuzzy logic approach to mitigate the critical data broadcast storm effect in VNDN. In the proposed scheme, vehicles are grouped into clusters using the K-means clustering algorithm, and then Cluster Heads (CHs) are selected using a fuzzy logic approach. A CH is uniquely responsible for broadcasting the critical data packets to all other vehicles in a cluster. A Gateway (GW) has the role of forwarding the critical data packets to the nearest clusters via their GWs. The simulation results show that the proposed scheme outperforms the naive method in terms of transmitted data packets and efficiency. The proposed scheme generates five times fewer data packets and achieves six times higher efficiency than the naive scheme. Full article

Context: In a social coding platform such as GitHub, a pull-request mechanism is frequently used by contributors to submit their code changes to reviewers of a given repository. In general, these code changes are either to add a new feature or to fix an existing bug. However, this mechanism is distributed and allows different contributors to submit unintentionally similar pull-requests that perform similar development activities. Similar pull-requests may be submitted to review in parallel time by different reviewers. This will cause redundant reviewing time and efforts. Moreover, it will complicate the collaboration process. Objective: Therefore, it is useful to assign similar pull-requests to the same reviewer to be able to decide which pull-request to choose in effective time and effort. In this article, we propose to group similar pull-requests together into clusters so that each cluster is assigned to the same reviewer or the same reviewing team. This proposal allows saving reviewing efforts and time. Method: To do so, we first extract descriptive textual information from pull-requests content to link similar pull-requests together. Then, we employ the extracted information to find similarities among pull-requests. Finally, machine learning algorithms (K-Means clustering and agglomeration hierarchical clustering algorithms) are used to group similar pull-requests together. Results: To validate our proposal, we have applied it to twenty popular repositories from public dataset. The experimental results show that the proposed approach achieved promising results according to the well-known metrics in this subject: precision and recall. Furthermore, it helps to save the reviewer time and effort. Conclusion: According to the obtained results, the K-Means algorithm achieves 94% and 91% average precision and recall values over all considered repositories, respectively, while agglomeration hierarchical clustering performs 93% and 98% average precision and recall values over all considered repositories, respectively. Moreover, the proposed approach saves reviewing time and effort on average between (67% and 91%) by K-Means algorithm and between (67% and 83%) by agglomeration hierarchical clustering algorithm. Full article

Port tugs are an important element in port activity and navigational safety issues. Port tugs ensure the safety of big ships while they are entering, manoeuvring, mooring and unmooring, and are of huge importance during other port operations. At the same time, optimizing the number of port tugs and tug bollard pull is also important from a port navigational safety and economic point of view. Calculation and evaluation methods of the optimal request for tugs bollard pull, in particular, port operations, are very important in order to guarantee the navigational safety of the port and ships during the main ship operations in the port. This article provides the number of requested port tugs and bollard pull calculation and evaluation methods on the basis of forces and moments acting on ships. On the basis of real ship voyages and manoeuvring at ports data as well as high accuracy simulators, theoretical methods were used, which were followed by our conclusions and recommendations, which can be used by port harbour masters and tug companies. Modern tugs have become an important element and integral part of modern port navigational safety. Such modern port tugs are also used for navigational safety and other important port functions and activities, such as fire protection and search and rescue operations. The optimal number and capacity evaluation of port tugs depending on port capacity and conditions are studied in this article. Full article

Software collaboration platforms where millions of developers from diverse locations can contribute to the common open source projects have recently become popular. On these platforms, various information is obtained from developer activities that can then be used as developer metrics to solve a variety of challenges. In this study, we proposed new developer metrics extracted from the issue, commit, and pull request activities of developers on GitHub. We created developer metrics from the individual activities and combined certain activities according to some common traits. To evaluate these metrics, we created an item-based project recommendation system. In order to validate this system, we calculated the similarity score using two methods and assessed top-n hit scores using two different approaches. The results for all scores with these methods indicated that the most successful metrics were binary_issue_related, issue_commented, binary_pr_related, and issue_opened. To verify our results, we compared our metrics with another metric generated from a very similar study and found that most of our metrics gave better scores that metric. In conclusion, the issue feature is more crucial for GitHub compared with other features. Moreover, commenting activity in projects can be equally as valuable as code contributions. The most of binary metrics that were generated, regardless of the number of activities, also showed remarkable results. In this context, we presented improvable and noteworthy developer metrics that can be used for a wide range of open-source software development challenges, such as user characterization, project recommendation, and code review assignment. Full article

The increasing installation numbers of ventilation units in residential buildings are driven by legal objectives to improve their energy efficiency. The dimensioning of a ventilation system for nearly zero energy buildings is usually based on the air flow rate desired by the clients or requested by technical regulations. However, this does not necessarily lead to a system actually able to renew the air volume of the living space effectively. In recent years decentralised systems with an alternating operation mode and fairly good energy efficiencies entered the market and following question was raised: “Does this operation mode allow an efficient air renewal?” This question can be answered experimentally by performing a tracer gas analysis. In the presented study, a total of 15 preliminary tests are carried out in a climatic chamber representing a single room equipped with two push-pull devices. The tests include summer, winter and isothermal supply air conditions since this parameter variation is missing till now for push-pull devices. Further investigations are dedicated to the effect of thermal convection due to human heat dissipation on the room air flow. In dependence on these boundary conditions, the determined air exchange efficiency varies, lagging behind the expected range 0.5 < ${\mathsf{\epsilon }}^{\mathrm{a}}$ < 1 in almost all cases, indicating insufficient air exchange including short-circuiting. Local air exchange values suggest inhomogeneous air renewal depending on the distance to the indoor apertures as well as the temperature gradients between in- and outdoor. The tested measurement set-up is applicable for field measurements. Full article

It is expected in a near future that safety applications based on vehicle-to-everything communications will be a common reality in the traffic roads. This technology will contribute to improve the safety of vulnerable road users, for example, with the use of virtual traffic light systems (VTLS) in the intersections. This work implements and evaluates a VTLS conceived to help the pedestrians pass safely the intersections without real traffic lights. The simulated VTLS scenario used two distinct communication paradigms—the pull and push communication models. The pull model was implemented in named data networking (NDN), because NDN uses natively a pull-based communication model, where consumers send requests to pull the contents from the provider. A distinct approach is followed by the push-based model, where consumers subscribe previously the information, and then the producers distribute the available information to those consumers. Comparing the performance of the push and pull models on a VANET with VTLS, it is observed that the push mode presents lower packet loss and generates fewer packets, and consequently occupies less bandwidth, than the pull mode. In fact, for the considered metrics, the VTLS implemented with the pull mode presents no advantage when compared with the push mode. Full article

Named data networking (NDN) is a revolutionary approach to cater for modern and future Internet usage trends. The advancements in web services, social networks and cloud computing have shifted Internet utilization towards information delivery. Information-centric networking (ICN) enables content-awareness in the network layer and adopts name-based routing through the NDN architecture. Data delivery in NDN is receiver-driven pull-based and governed by requests (interests) sent out by the receiver. The ever-increasing share of high-volume media streams traversing the Internet due to the popularity and availability of video-streaming services can put a strain on network resources and lead to congestion. Since most congestion control techniques proposed for NDN are receiver-based and rely on the users to adjust their interest rates, a fairness scheme needs to be implemented at the intermediate network nodes to ensure that “rogue” users do not monopolize the available network resources. This paper proposes a fairness-based active queue management at network routers which performs per-flow interest rate shaping in order to ensure fair allocation of resources. Different congestion scenarios for both single path and multipath network topologies have been simulated to test the effectiveness of the proposed fairness scheme. Performance of the scheme is evaluated using Jain’s fairness index as a fairness metric. Full article

By design, Named Data Networking (NDN) supports pull-based traffic, where content is retrieved only upon consumer request. However, some of the use cases (i.e., emergency situations) in the Internet of Things (IoT) requires push-based traffic, where a producer broadcasts the data based on the emergency situation without any consumer request. Therefore, it is necessary to modify the existing NDN forwarding engine when designing for an IoT scenario. Although solutions are provided to enable push-based traffic in IoT, the main solutions in the current literature lack data broadcast control design. Moreover, the existing solutions use an additional interest messages exchange, which creates extra overheads in the network, thereby resulting in higher delay and lower throughput. In this paper, therefore, we propose a name-based push-data broadcast control scheme for IoT systems, and consider two scenarios, i.e., smart buildings and vehicular networks. The proposed scheme consists of a robust content namespace design, device namespace design, and minor amendments to the data packet format and unsolicited data policy of the forwarding engine as well. The evaluation is carried out for both scenarios. Simulation experiments show that the proposed scheme outperforms the recent proposed schemes in terms of total number of data packets processed in the network, total energy consumption, and average delay in the network by varying the number of data packets per 2 s and varying vehicle speed. Full article