ABSTRACT Introduction Open‐source software (OSS) projects thrive on collaborative communities, forming a critical backbone of modern technology. Despite community contributions shaping open‐source development, a gap remains in understanding how community interactions impact project longevity. To address this, our study investigates how discussion volume (as a measure of engagement), sentiment expressed in discussions, issue resolution time, and issue characteristics influence both the resolution process and, crucially, OSS project lifespan. Methods Using a comprehensive GitHub issue dataset, we applied robust statistical analyses and effect size measures across various project lifecycle stages. Results Our findings reveal that while discussion volume weakly correlates with resolution time and sentiment's practical impact is negligible, a counter‐intuitive pattern emerges: longer‐lived projects consistently exhibit extended median issue resolution times, in contrast to faster initial resolutions observed in shorter‐lived projects. Analysis of issue labels suggests this is because shorter‐lived projects tend to address beginner‐friendly issues, whereas enduring projects confront more complex, core development tasks. These findings emphasize that OSS project longevity depends less on rapid initial issue resolution and more on structured, sustained engagement with increasingly complex contributions–underscoring the vital role of qualitative communication and the nature of work in fostering robust communities.

Abstract Static machine and deep learning algorithms are commonly used in intrusion detection systems (IDSs). However, their effectiveness is constrained by the evolving data distribution and the obsolescence of the static data sources used for model training. Consequently, static classifiers lose efficacy, necessitating expensive model retraining with time. The aim is to develop a dynamic and adaptable IDS that mitigates the limitations of static models, ensuring real-time threat detection and reducing the need for frequent, resource-intensive model retraining. This research proposes an approach that amalgamates the adaptive random forest (ARF) classifier with Hoeffding’s bounds and a moving average test for the early and accurate detection of network intrusions. The ARF can adapt in real time to shifting network conditions and evolving attack patterns, constantly refining its intrusion detection capabilities. Furthermore, the inclusion of Hoeffding’s bounds and the moving average test adds a dimension of statistical rigor to the system, facilitating the timely recognition of concept drift and distinguishing benign network variations from potential intrusions. The synergy of these techniques results in reduced false positives and false negatives, thereby enhancing the overall detection rate. The proposed method delivers outstanding results, with 99.95% accuracy and an impressive 99.96% recall rate on the latest CIC-IDS 2018 dataset, outperforming the results of existing approaches.

Abstract Context Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise.

Abstract Open Source Software (OSS) projects have long been studied to understand the need of community support for their growth and survival. However, there has been limited research on developers' abandonment, though it is found to have detrimental effect on quality and sustainability of OSS projects. This paper examines the impact of developer and project‐related factors on developer abandonment in OSS projects. Factors include developer attributes: experience, role, coding language, and joining date, and project attributes: change complexity and sentiments in commit logs. The findings are (1) for developer experience, core/non‐core member, and joining date; there exists a pattern of developers abandoning the projects. (2) There is no definite answer for coding language. (3) It does not relate to the change profile of a project except a few indicators. Sentiments can also not be linked. These results provide OSS community and researchers with useful insights on developer abandonment and the factors influencing it or not.

Open Source Software (OSS) has become ubiquitous nowadays. It is crucial for the OSS project managers as well as developers to understand users' perception of quality to remain consistent with producing good quality software. To understand users' point of view, like many studies in the commercial product/service sectors which rely upon customer reviews to understand customer behaviour, the authors' main focus is to analyse user ratings and reviews of OSS projects that may represent user satisfaction for that particular software application. We have analysed 41,428 customer reviews (obtained from SourceForge.net) of 886 most popular OSS projects belonging to a specific domain and programming language. The results indicate that overall user ratings and reviews of the popular OSS projects contain a very positive sentiment and more frequent occurrence of emotions like joy, anticipation, and trust as compared to disgust, fear, and surprise. Further, we have examined that the affectiveness of customer reviews with respect to OSS popularity and quality aspects along with their programming languages and problem domains. The results show a stronger association of review affectiveness with the number of reviews than with the number of downloads of the OSS projects, and more downloads do not mean more reviews.

Abstract Context Community‐based collaborative approach in open source software paradigm promotes reuse of existing software packages. There are several repositories (e.g., npm) for packages and have their own set of metrics for ranking. Objective This study explores the diversity of different popularity metrics and also the relationship between popularity metrics and development activity of the packages. Another aim is to create a package popularity index by aggregating a set of noncollinear popularity metrics. Method Using 195 K packages from different repositories, we investigated the correlation between different popularity metrics. K‐medoids algorithm helped to identify packages with different levels of popularity. Random forests method is utilized to create the package popularity index. Lastly, we used scikit‐learn implementation for determining feature importance in the model. Results Popularity metrics of the Github platform are very strongly correlated (R ≥ 0.85) for highly popular packages. Popular packages have high‐development activity. However, the number of downloads of a package does not associate with development activity. Not all the metrics are important for determining popularity of a software package. Conclusion This study provides practical guidelines to understand important metrics to determine the popularity of software packages. Researchers should focus on non‐collinear metrics, thereby avoiding similar metrics while aggregating for building models.

In Services Oriented Computing, a composite web service is a value-added service composed of loosely coupled, independent, and distributed component web services. Component or partner web services jointly contribute to fulfil functional as well as non-functional requirements of users of a composite web service. One of the fundamental challenges in Services Oriented Computing is to ensure that a composite web service is flexible enough to react to changes (QoS degradation) in its partner web services at the time of execution. In this context, it is important that the time to adapt to changes should not be significant. Several solutions exist for run-time monitoring of partner web services so that clients can replace them with better alternatives when their QoS values degrade. But these solutions follow either a reactive approach (which is time consuming), or a prediction-based proactive approach (again time consuming, and moreover predicted events may never happen). This article proposes a solution using a publish/subscribe mechanism which is distributed between web service clients and the service providers, and follows a proactive preventive approach. It uses mobile agents to communicate partner web service's QoS status to its clients just in time, in order to decide to choose an alternative in case the QoS values are not satisfactory. The prototype is implemented using JAVA and Java Agent DEvelopment framework (JADE) programming languages. The experimental results show effectiveness of the proposed approach when compared with a static approach (the benchmark), as well as with a reactive solution. Moreover, the framework performs well even in the wake of the increasing levels of QoS degradation of partner web services.

In real world Service Oriented Software Systems, changes to web services infrastructure are not an exception but a rule. Timely detection and a fast reaction to handle these changes are important to reduce the impact. This paper proposes an agent based framework to manage the situation when third party web services become unavailable. It uses the concept of mobile agents to monitor web services on the provider side. The framework is implemented using Advanced JAVA and JADE environment. Experimental analysis shows that the proposed solution is both effective and efficient. In future, the framework will be extended to include monitoring of other Quality of Service properties.

Component based software development approach makes use of already existing software components to build new applications. Software components may be available in-house or acquired from the global market. One of the most critical activities in this reuse based process is the selection of appropriate components. Component evaluation is the core of the component selection process. Component quality models have been proposed to decide upon a criterion against which candidate components can be evaluated and then compared. But none is complete enough to carry out the evaluation. It is advocated that component users need not bother about the internal details of the components. But we believe that complexity of the internal structure of the component can help estimating the effort related to evolution of the component. In our ongoing research, we are focusing on quality of internal design of a software component and its relationship to the external quality attributes of the component.