Open Source Software Projects Research Articles

A Comprehensive Review of Recent Research Trends on Unmanned Aerial Vehicles (UAVs)

The growing interest in unmanned aerial vehicles (UAVs) from both the scientific and industrial sectors has attracted a wave of new researchers and substantial investments in this expansive field. However, due to the wide range of topics and subdomains within UAV research, newcomers may find themselves overwhelmed by the numerous options available. It is therefore crucial for those involved in UAV research to recognize its interdisciplinary nature and its connections with other disciplines. This paper presents a comprehensive overview of the UAV field, highlighting recent trends and advancements. Drawing on recent literature reviews and surveys, the review begins by classifying UAVs based on their flight characteristics. It then provides an overview of current research trends in UAVs, utilizing data from the Scopus database to quantify the number of scientific documents associated with each research direction and their interconnections. This paper also explores potential areas for further development in UAVs, including communication, artificial intelligence, remote sensing, miniaturization, swarming and cooperative control, and transformability. Additionally, it discusses the development of aircraft control, commonly used control techniques, and appropriate control algorithms in UAV research. Furthermore, this paper addresses the general hardware and software architecture of UAVs, their applications, and the key issues associated with them. It also provides an overview of current open source software and hardware projects in the UAV field. By presenting a comprehensive view of the UAV field, this paper aims to enhance our understanding of this rapidly evolving and highly interdisciplinary area of research.

A resilience‐based framework for assessing the evolution of open source software projects

AbstractOpen source software (OSS) has been developing for more than two decades. It originated as a movement with the introduction of the first free/libre OSS operating system, became a popular trend among the developer community, led to enterprise solutions widely embraced by the global market, and began garnering attention from significant players in the software industry (such as IBM's acquisition of RedHat). Throughout the years, numerous software assessment models have been suggested, some of which were created specifically for OSS projects. Most of these assessment models focus on software quality and maintainability. Some models are taking under consideration health aspects of OSS projects. Despite the multitude of these models, there is yet to be a universally accepted model for assessing OSS projects. In this work, we aim to adapt the City Resilience Framework (CRF) for use in OSS projects to establish a strong theoretical foundation for OSS evaluation focusing on the project's resilience as it evolves over time. We would like to highlight that our goal with the proposed assessment model is not to compare two OSS solutions with each other, in terms of resilience, or even do a resilience ranking between the available OSS tools. We are aiming to investigate resilience of an OSS project as it evolves and identify possible opportunities of improvements in the four dimensions we are defining. These dimensions are as follows: source code, business and legal, integration and reuse, and social (community). The CRF is a framework that was introduced to measure urban resilience and most specifically how cities' resilience is changing as they evolve. We believe that a software evaluation model that focuses on resilience can complement the pre‐existing models based on software quality and software health. Although concepts that are related to resilience, like sustainability or viability, already appear in literature, to our best knowledge, there is no OSS assessment model that evaluates the resilience of an OSS project. We argue that cities and OSS projects are both dynamically evolving systems with similar characteristics. The proposed framework utilizes both quantitative and qualitative indicators, which is viewed as an advantage. Lastly, we would like to emphasize that the framework has been tested on the enterprise software domain as part of this study, evaluating five major versions of six OSS projects, Laravel, Composer, PHPMyAdmin, OKApi, PatternalPHP, and PHPExcel, the first three of which are intuitively considered resilient and the three latter nonresilient, to provide a preliminary validation of the models' ability to distinguish between resilient and not resilient projects.

Open-Source Machine Learning in Computational Chemistry.

The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community.

Accelerating Open Modification Spectral Library Searching on Tensor Core in High-dimensional Space.

Driven by technological advances, the throughput and cost of mass spectrometry proteomics experiments have improved by orders of magnitude in recent decades. Spectral library searching is a common approach to annotating experimental mass spectra by matching them against large libraries of reference spectra corresponding to known peptides. An important disadvantage, however, is that only peptides included in the spectral library can be found, whereas novel peptides, such as those with unexpected post-translational modifications, will remain unknown. Open modification searching is an increasingly popular approach to annotate modified peptides based on partial matches against their unmodified counterparts. Unfortunately, this leads to very large search spaces and excessive runtimes, which is especially problematic considering the continuously increasing sizes of mass spectrometry proteomics datasets. We propose an open modification searching algorithm, called HOMS-TC, that fully exploits parallelism in the entire pipeline of spectral library searching. We designed a new highly parallel encoding method based on the principle of hyperdimensional computing to encode mass spectral data to hypervectors while minimizing information loss. This process can be easily parallelized since each dimension is calculated independently. HOMS-TC processes two stages of existing cascade search in parallel and selects the most similar spectra while considering post-translational modifications. We accelerate HOMS-TC on NVIDIA's Tensor Core Units, which is emerging and readily available in the recent graphics processing unit (GPU). Our evaluation shows that HOMS-TC is 31 × faster on average than alternative search engines and provides comparable accuracy to competing search tools. HOMS-TC is freely available under the Apache 2.0 license as an open-source software project at https://github.com/tycheyoung/homs-tc.

Hierarchical and Hybrid Organizational Structures in Open-source Software Projects: A Longitudinal Study

Despite the absence of a formal process and a central command-and-control structure, developer organization in open-source software (OSS) projects are far from being a purely random process. Prior work indicates that, over time, highly successful OSS projects develop a hybrid organizational structure that comprises a hierarchical part and a non-hierarchical part. This suggests that hierarchical organization is not necessarily a global organizing principle and that a fundamentally different principle is at play below the lowest positions in the hierarchy. Given the vast proportion of developers are in the non-hierarchical part, we seek to understand the interplay between these two fundamentally differently organized groups, how this hybrid structure evolves, and the trajectory individual developers take through these structures over the course of their participation. We conducted a longitudinal study of the full histories of 20 popular OSS projects, modeling their organizational structures as networks of developers connected by communication ties and characterizing developers’ positions in terms of hierarchical (sub)structures in these networks. We observed a number of notable trends and patterns in the subject projects: (1) hierarchy is a pervasive structural feature of developer networks of OSS projects; (2) OSS projects tend to form hybrid organizational structures, consisting of a hierarchical and a non-hierarchical part; and (3) the positional trajectory of a developer starts loosely connected in the non-hierarchical part and then tightly integrate into the hierarchical part, which is associated with the acquisition of experience (tenure), in addition to coordination and coding activities. Our study (a) provides a methodological basis for further investigations of hierarchy formation, (b) suggests a number of hypotheses on prevalent organizational patterns and trends in OSS projects to be addressed in further work, and (c) may ultimately guide the governance of organizational structures.

Open-source Software Reliability Modeling with Stochastic Impulsive Differential Equations

In reality, sudden updates of software, attacks of hackers, influence of the Internet market, etc. can cause a surge in the number of open-source software (OSS) faults (this moment is the time when impulse occurs), which results in impulsive phenomenon. For the existing software reliability models, dynamic process of software fault is considered to be continuous when assessing reliability, but continuity of the process can be disrupted with appearance of random impulses. Thus, to more accurately assess software reliability, we proposed an OSS reliability model with SIDE. In the model, dynamic process of software fault is divided into a continuous and a skipped part, described the continuous part of the process with SDE, and described destruction of the continuity caused by unpredictable random events with random impulses. Finally, the proposed model is verified with two datasets from real OSS project, and the results show that the proposed model is more in line with reality and has better fitting effect than the existing models.

The influence of project initiators’ person-to-person followership on project popularity in open source communities: The role of reach and importance

Person-to-person (P2P) followership is an important aspect of major open source software (OSS) development platforms. In this age of social media platforms, P2P followership has significantly shaped the way users engage in OSS development by facilitating the establishment of connections among OSS users. Despite the prevalence of P2P followership, less is known about the impact of OSS project initiators’ P2P followership nodes on their project popularity. This is a particularly important gap considering the low rate of OSS project success. We posit that OSS project initiators derive information and influence benefits from the quantity and connectivity of their P2P followership nodes, explaining the popularity of the projects initiated. We determine the connectivity of OSS project initiators’ P2P followership nodes based on the nodes’ reach and importance. To test the hypotheses, we use a large panel dataset collected over 24 months from GitHub. The findings indicate that the quantity of OSS project initiators’ P2P followership nodes including followers and followees has a positive effect on their project popularity. Moreover, such an effect is mostly dependent on the connectivity of the OSS project initiators’ P2P followership nodes in such a way that highly connected P2P followership nodes do not impact influence benefits but they increase information benefits. We discuss the theoretical and practical implications of our findings.

A Microstructural Approach to Self-Organizing: The Emergence of Attention Networks

A recent line of inquiry investigates new forms of organizing as bundles of novel solutions to universal problems of resource allocation and coordination: how to allocate organizational problems to organizational participants and how to integrate participants’ resulting efforts. We contribute to this line of inquiry by reframing organizational attention as the outcome of a concatenation of self-organizing, microstructural mechanisms linking multiple participants to multiple problems, thus giving rise to an emergent attention network. We argue that, when managerial hierarchies are absent and authority is decentralized, observable acts of attention allocation produce interpretable signals that help participants to direct their attention and share information on how to coordinate and integrate their individual efforts. We theorize that the observed structure of an organizational attention network is generated by the concatenation of four interdependent micromechanisms: focusing, reinforcing, mixing, and clustering. In a statistical analysis of organizational problem solving within a large open-source software project, we find support for our hypotheses about the self-organizing dynamics of the observed attention network connecting organizational problems (software bugs) to organizational participants (volunteer contributors). We discuss the implications of attention networks for theory and practice by emphasizing the self-organizing character of organizational problem solving. We discuss the generalizability of our theory to a wider set of organizations in which participants can freely allocate their attention to problems and the outcomes of their allocation are publicly observable without cost. Funding: Financial support for this work was provided by the Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung [Grant 100018_150126] (“Relational event modes for bipartite networks with application to collaborative problem solving,” P.I. Alessandro Lomi) and by the Deutsche Forschungsgemeinschaft [Grant 321869138] (“Statistical analysis of time-stamped multi-actor events in social networks,” P.I. Jüergen Lerner). Supplemental Material: The supplemental video containing the dynamic visualization of the data is available at https://zenodo.org/record/7564503 and in the e-companion (available at https://doi.org/10.1287/orsc.2023.1674 ).

ViralConsensus: A fast and memory-efficient tool for calling viral consensus genome sequences directly from read alignment data.

In viral molecular epidemiology, reconstruction of consensus genomes from sequence data is critical for tracking mutations and variants of concern. However, as the number of samples that are sequenced grows rapidly, compute resources needed to reconstruct consensus genomes can become prohibitively large. ViralConsensus is a fast and memory-efficient tool for calling viral consensus genome sequences directly from read alignment data. ViralConsensus is orders of magnitude faster and more memory-efficient than existing methods. Further, unlike existing methods, ViralConsensus can pipe data directly from a read mapper via standard input and performs viral consensus calling on-the-fly, making it an ideal tool for viral sequencing pipelines. ViralConsensus is freely available at https://github.com/niemasd/ViralConsensus as an open-source software project. Supplementary data are available at Bioinformatics online.

Understanding Mentors’ Engagement in OSS Communities via Google Summer of Code

A constant influx of newcomers is essential for the sustainability and success of open source software (OSS) projects. However, successful onboarding is always challenging because newcomers face various initial contributing barriers. To support newcomer onboarding, OSS communities widely adopt the mentoring approach. Despite its significance, previous mentoring studies tend to focus on the newcomer’s perspective, leaving the mentor’s perspective relatively under-studied. To better support mentoring, we study the popular Google Summer of Code (GSoC). It is a well-established global program that offers stipends and mentors to students aiming to bring more student developers into OSS development. We combine online data analysis, an email survey, and semi-structured interviews with the GSoC mentors to understand their motivations, challenges, strategies, and gains. We propose a taxonomy of GSoC mentors’ engagement with four themes, ten categories, 34 sub-categories, and 118 codes, as well as the mentors’ attitudes toward the codes. In particular, we find that mentors participating in GSoC are primarily intrinsically motivated, and some new motivators emerge adapting to the contemporary challenges, e.g., sustainability and advertisement of projects. Forty-one challenges and 52 strategies associated with the program timeline are identified, most of which are first time revealed. Although almost all the challenges are agreed upon by specific mentors, some mentors believe that several challenges are reasonable and even have a positive effect. For example, the cognitive differences between mentors and mentees can stimulate new perspectives. Most of the mentors agreed that they had adopted these strategies during the mentoring process, but a few strategies recommended by the GSoC administration were not agreed upon. Self-satisfaction, different skills, and peer recognition are the main gains of mentors to participate in GSoC. Eventually, we discuss practical implications for mentors, students, OSS communities, GSoC programs, and researchers.

A taxonomy for mining and classifying privacy requirements in issue reports

Digital and physical footprints are a trail of user activities collected over the use of software applications and systems. As software becomes ubiquitous, protecting user privacy has become challenging. With the increasing of user privacy awareness and advent of privacy regulations and policies, there is an emerging need to implement software systems that enhance the protection of personal data processing. However, existing privacy regulations and policies only provide high-level principles which are difficult for software engineers to design and implement privacy-aware systems. In this paper, we develop a taxonomy that provides a comprehensive set of privacy requirements based on two well-established and widely-adopted privacy regulations and frameworks, the General Data Protection Regulation (GDPR) and the ISO/IEC 29100. These requirements are refined into a level that is implementable and easy to understand by software engineers, thus supporting them to attend to existing regulations and standards. We have also performed a study on how two large open-source software projects (Google Chrome and Moodle) address the privacy requirements in our taxonomy through mining their issue reports. The paper discusses how the collected issues were classified, and presents the findings and insights generated from our study.

Microarray Gene Expression Data Generation and Pre-Processing of Moringa Oleifera Leaves for the Improvement of Medicinal Use

Moringa oleifera is a plant species belonging to the family name called Moringaceae widely cultivated for human use. This study aimed to generate microarray gene expression data from the leaves of the Moringa oleifera plant and explore the usage of some tools available in the Bioconductor R package for the quality control. Six (6) young Moringa Oleifera leaves (YMOL) samples and six (6) old Moringa oleifera leaves (OMOL) samples were collected from the plant and processed for microarray data generation. Microarray gene expression raw data from the leaves of the Moringa oleifera plants were generated, each in a CEL file format and the usage of some tools available in R programming Bioconductor open source and development software project were explored for the quality control of the data. Affycoretools were installed in the R environment for pre-processing of microarray raw data. AffyQCReport tools were used to generate a comprehensive quality control (QC) report for the microarray unnormalized raw data in PDF format. It is recommended that Gene chip robust multiarray analysis (GCRMA) method can be used for visual inspection, background correction, normalization and summarization of this microarray raw data. The normalized microarray raw data can be used through the genetic engineering to improve the Moringa oleifera plant medicinal values in order to solve some medical problems especially with patients suffering from diabetes and hypertension and also can be of enormous importance in the fields of pharmacy and medicine at large.

Log-related Coding Patterns to Conduct Postmortems of Attacks in Supervised Learning-based Projects

Adversarial attacks against supervised learning a algorithms, which necessitates the application of logging while using supervised learning algorithms in software projects. Logging enables practitioners to conduct postmortem analysis, which can be helpful to diagnose any conducted attacks. We conduct an empirical study to identify and characterize log-related coding patterns, i.e., recurring coding patterns that can be leveraged to conduct adversarial attacks and needs to be logged. A list of log-related coding patterns can guide practitioners on what to log while using supervised learning algorithms in software projects. We apply qualitative analysis on 3,004 Python files used to implement 103 supervised learning-based software projects. We identify a list of 54 log-related coding patterns that map to six attacks related to supervised learning algorithms. Using Lo g Assistant to conduct P ostmortems for Su pervised L earning ( LOPSUL ) , we quantify the frequency of the identified log-related coding patterns with 278 open-source software projects that use supervised learning. We observe log-related coding patterns to appear for 22% of the analyzed files, where training data forensics is the most frequently occurring category.

An empirical examination of newcomer contribution costs in established OSS communities: a knowledge-based perspective

PurposeTo remain sustainable, open source software (OSS) projects must attract new members—or newcomers—who make contributions. In this paper, the authors develop a set of hypotheses based on the knowledge barriers framework that examines how OSS communities can encourage contributions from newcomers.Design/methodology/approachEmploying longitudinal data from the source code repositories of 232 OSS projects over a two-year period, the authors employ a Poisson-based mixed model to test how community characteristics, such as the main drivers of knowledge-based costs, relate to newcomers' contributions.FindingsThe results indicate that community characteristics, such as programming language choice, documentation effort and code structure instability, are the main drivers of knowledge-based contribution costs. The findings also suggest that managing these costs can result in more inclusive OSS communities, as evidenced by the number of contributing newcomers; the authors highlight the importance of maintaining documentation efforts for OSS communities.Originality/valueThis paper assumes that motivational factors are a necessary but insufficient condition for newcomer participation in OSS projects and that the cost to participation should be considered. Using the knowledge barriers framework, this paper identifies the main knowledge-based costs that hinder newcomer participation. To the best of the authors' knowledge, this is the first empirical study that does not limit data collection to a single hosting platform (e.g., SourceForge), which improves the generalizability of the findings.

A Comprehensive Investigation of the Impact of Class Overlap on Software Defect Prediction

Software Defect Prediction (SDP) is one of the most vital and cost-efficient operations to ensure the quality of software under developed. The performance of SDP heavily relies on the characteristics of experimental datasets (or say SDP datasets). However, there often exists the phenomenon of class overlap in the SDP datasets, i.e., defective modules and non-defective modules are similar in terms of values of metrics. Class overlap hinders the smooth performance as well as the use of SDP models. Even though efforts have been made to investigate the impact of overlapping instance removing techniques on the performance of SDP, many open issues are still challenging yet unknown. For example, 1) how to effectively identify the overlapping instances? 2) Whether is the phenomenon of class overlap universal in the SDP datasets? 3) What are the impacts of class overlap on the performance and interpretation of SDP models? Questions like these are very important but have not been fully explored yet. In this paper, we conduct an empirical study to comprehensively investigate the impact of class overlap on SDP. Specifically, we first propose an overlapping instances identification approach by analyzing the class distribution in the local neighborhood of a given instance. Based on the approach, we then investigate the impact of class overlap on the performance and the interpretation of seven representative SDP models. Finally, we investigate the impact of two common overlapping instance handling techniques (i.e., removing and separating techniques) on the performance of SDP models. Through an extensive case study on 230 datasets that span across industrial and open-source software projects, we observe that: i) 70.0% of SDP datasets contain overlapping instances; ii) different levels of class overlap have different impacts on the performance of SDP models. The class overlap ratio and the number of instances seriously affect the stability of the performance of SDP models; iii) class overlap affects the rank of the important feature list of SDP models, particularly the feature lists at the top 2 and top 3 ranks; IV) Class overlap handling techniques could statistically significantly improve the performance of SDP models trained on datasets with over 12.5% overlap ratios. Therefore, on the basis of these findings we suggest that future work in SDP should apply our proposed KNN method to: i) identify whether the overlap ratios of their defect datasets are greater than 12.5% before building SDP models; ii) remove the overlapping instances to find the more consistent guiding significance metrics; iii) combine RF classifier and class overlap handling techniques when reducing the efforts to review codes.

To Follow or Not to Follow: Understanding Issue/Pull-Request Templates on GitHub

For most Open Source Software (OSS) projects, issues and Pull-requests (PR) are the primary means by which stakeholders of a project report and discuss software problems and code changes, and their descriptions are important for people to understand them. To help ensure the informational quality of issue/PR descriptions, GitHub introduced the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">issue/PR template</i> feature, which pre-populates the description for anyone trying to open a new issue/PR. To better understand this feature, we report on a large-scale, mixed-methods empirical study of templates that explores contents, impacts, and perceptions. Our results show that templates typically contain elements to greet contributors, explain project guidelines, and collect relevant information. After template adoption, the monthly volume of incoming issues and PRs decreases, and issues have fewer monthly discussion comments and longer resolution duration. Although both contributors and maintainers positively rated the usefulness of templates from various aspects, they also reported challenges in using templates ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</i> . excessive and irrelevant information request) and suggested potential improvements of the template feature ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</i> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</i> . better user interaction and advanced automation). This work contributes to the informed use and targeted improvement of templates to enhance OSS practitioners' collaboration and interaction.

DupHunter: Detecting Duplicate Pull Requests in Fork-Based Development

The emergence of numerous fork-based development platforms facilitates the development of Open-Source Software (OSS) projects. Developers across the world can fork software projects and submit their Pull Requests (PRs) to the projects. However, as the number of forks increases, numerous duplicate PRs might be submitted. These duplicate PRs may cause extra code review workload and frustrate developers working on the projects. To detect duplicate PRs, many approaches have been proposed, which analyze the similarity of different elements in PRs. However, previous approaches still suffer from unsatisfied detection accuracy due to two challenges. That is, they ignore the syntactic structural information of text elements in PRs and lack the joint reasoning between different elements of two PRs. In this study, we propose an automated duplicate PRs detector named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DupHunter</i> ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dup</b> licate PRs <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hunter</i> ), which includes a graph embedding component and a duplicate PRs detection component to address the above challenges. The graph embedding component uses a feature graph to represent a PR. It encodes the syntactic structure and semantics of text elements (e.g., the title and the description), as well as the knowledge of non-text elements (e.g., the submission time), to address the syntactic structural information challenge. The duplicate PRs detection component tackles the joint reasoning challenge using a graph matching network, which enables the information exchange and matching across different elements of two feature graphs with an attention coefficient mechanism. Experiments on 26 open-source projects show that DupHunter achieves an average <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F1-score@1</i> value of 0.650, significantly outperforming the state-of-the-art approaches by 3.2% to 48.1%. DupHunter can accurately detect duplicate PRs, with an average <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Precision@1</i> value of 0.922 and an average <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Recall@1</i> value of 0.502.

A Software Requirements Ecosystem: Linking Forum, Issue Tracker, and FAQs for Requirements Management

User feedback is an important resource in modern software development, often containing requirements that help address user concerns and desires for a software product. The feedback in online channels is a recent focus for software engineering researchers, with multiple studies proposing automatic analysis tools. In this work, we investigate the product forums of two large open source software projects. Through a quantitative analysis, we show that forum feedback is often manually linked to related issue tracker entries and product documentation. By linking feedback to their existing documentation, development teams enhance their understanding of known issues, and direct their users to known solutions. We discuss how the links between forum, issue tracker, and product documentation form a requirements ecosystem that has not been identified in the previous literature. We apply state-of-the-art deep-learning to automatically match forum posts with related issue tracker entries. Our approach identifies requirement matches with a mean average precision of 58.9% and hit ratio of 82.2%. Additionally, we apply deep-learning using an innovative clustering technique, achieving promising performance when matching forum posts to related product documentation. We discuss the possible applications of these automated techniques to support the flow of requirements between forum, issue tracker, and product documentation.

CrossFix: Resolution of GitHub issues via similar bugs recommendation

SummaryWith the increasing popularity of Open‐Source Software (OSS), the number of GitHub issues reported daily in these OSS projects has been growing rapidly. To resolve these issues, developers need to spend time and effort in debugging and fixing these issues. Meanwhile, a recent approach shows that similar bugs exist across different projects, and one could use the GitHub issues from a different project for finding new bugs for a related project. To locate similar bugs for our approach, we first conduct a study of similar bugs in GitHub. Our study redefines similar bugs as bugs that share the (1) same libraries, (2) same functionalities, (3) same reproduction steps, (4) same configurations, (5) same outcomes, or (6) same errors. Moreover, our study revealed the usefulness of similar bugs in helping developers to find more contexts about the bug and fixing. Based on our study, we design CrossFix, a tool that automatically suggests relevant GitHub issues based on an open GitHub issue. The suggested GitHub issues may contain solutions written in natural language or pull requests that help developers in resolving the given issue. Our evaluation on 249 open issues from Java and Android projects shows that CrossFix could suggest similar bugs to help developers in debugging and fixing.

Predicting Software Cohesion Metrics with Machine Learning Techniques

The cohesion value is one of the important factors used to evaluate software maintainability. However, measuring the cohesion value is a relatively difficult issue when tracing the source code manually. Although there are many static code analysis tools, not every tool measures every metric. The user should apply different tools for different metrics. In this study, besides the use of these tools, we predicted the cohesion values (LCOM2, TCC, LCC, and LSCC) with machine learning techniques (KNN, REPTree, multi-layer perceptron, linear regression (LR), support vector machine, and random forest (RF)) to solve them alternatively. We created two datasets utilizing two different open-source software projects. According to the obtained results, for the LCOM2 and TCC metrics, the KNN algorithm provided the best results, and for LCC and LSCC metrics, the REPTree algorithm was the best. However, out of all the metrics, RF, REPTree, and KNN had close performances with each other, and therefore any of the RF, REPTree, and KNN techniques can be used for software cohesion metric prediction.