Open-source Repository Research Articles

A Systematic Mapping Study on Quantum and Quantum-inspired Algorithms in Operations Research

Quantum and quantum-inspired algorithms have not yet been systematically classified in the context of potential Operations Research (OR) applications. Our systematic mapping is designed for quick consultation and shows which algorithms have been significantly explored in the context of OR, as well as which algorithms have been vaguely addressed in the same context. The study provides rapid access to OR professionals, both practitioners and researchers, who are interested in applying and/or further developing these algorithms in their respective contexts. We prepared a replicable protocol as a backbone of this systematic mapping study, specifying research questions, establishing effective search and selection methods, defining quality metrics for assessment, and guiding the analysis of the selected studies. A total of more than 2 000 studies were found, of which 149 were analyzed in detail. Readers can have an interactive hands-on experience with the collected data on an open-source repository with a website. An international standard was used as part of our classification, enabling professionals and researchers from across the world to readily identify which algorithms have been applied in any industry sector. Our effort also culminated in a rich set of takeaways that can help the reader identify potential paths for future work.

MORCoRA: Multi-Objective Refactoring Recommendation Considering Review Availability

Background: Search-based refactoring involves searching for a sequence of refactorings to achieve specific objectives. Although a typical objective is improving code quality, a different perspective is also required; the searched sequence must undergo review before being applied and may not be applied if the review fails or is postponed due to no proper reviewers. Aim: Therefore, it is essential to ensure that the searched sequence of refactorings can be reviewed promptly by reviewers who meet two criteria: (1) having enough expertise and (2) being free of heavy workload. The two criteria are regarded as the review availability of the refactoring sequence. Method: We propose MORCoRA, a multi-objective search-based technique that can search for code quality improvable, semantic preserved, and high review availability possessed refactoring sequences and corresponding proper reviewers. Results: We evaluate MORCoRA on six open-source repositories. The quantitative analysis reveals that MORCoRA can effectively recommend refactoring sequences that fit the requirements. The qualitative analysis demonstrates that the refactorings recommended by MORCoRA can enhance code quality and effectively address code smells. Furthermore, the recommended reviewers for those refactorings possess high expertise and are available to review. Conclusions: We recommend that refactoring recommenders consider both the impact on quality improvement and the developer resources required for review when recommending refactorings.

NCBench: providing an open, reproducible, transparent, adaptable, and continuous benchmark approach for DNA-sequencing-based variant calling

We present the results of the human genomic small variant calling benchmarking initiative of the German Research Foundation (DFG) funded Next Generation Sequencing Competence Network (NGS-CN) and the German Human Genome-Phenome Archive (GHGA). In this effort, we developed NCBench, a continuous benchmarking platform for the evaluation of small genomic variant callsets in terms of recall, precision, and false positive/negative error patterns. NCBench is implemented as a continuously re-evaluated open-source repository. We show that it is possible to entirely rely on public free infrastructure (Github, Github Actions, Zenodo) in combination with established open-source tools. NCBench is agnostic of the used dataset and can evaluate an arbitrary number of given callsets, while reporting the results in a visual and interactive way. We used NCBench to evaluate over 40 callsets generated by various variant calling pipelines available in the participating groups that were run on three exome datasets from different enrichment kits and at different coverages. While all pipelines achieve high overall quality, subtle systematic differences between callers and datasets exist and are made apparent by NCBench.These insights are useful to improve existing pipelines and develop new workflows. NCBench is meant to be open for the contribution of any given callset. Most importantly, for authors, it will enable the omission of repeated re-implementation of paper-specific variant calling benchmarks for the publication of new tools or pipelines, while readers will benefit from being able to (continuously) observe the performance of tools and pipelines at the time of reading instead of at the time of writing.

An Algorithm for Nutrient Mixing Optimization in Aquaponics

Controlled environment agriculture is a promising alternative to conventional production methods, as it is less affected by climate change and is often more sustainable, especially in circular and recycling frameworks such as aquaponics. A major cost factor in such facilities, however, is the need for skilled labor. Depending on available resources, there are endless possibilities on how to choose ingredients to realize a desired nutrient solution. At the same time, the composition of the desired solution is subject to fluctuations in fish water quality, fertilizer availability, weather, and plant development. In high-evaporation scenarios, e.g., summer, nutrient solutions might be mixed multiple times per day. This results in a complex, multi-variable task that is time-consuming to solve manually, yet requires frequent resolution. This work aims to help solve this challenge by providing methods to automate the nutrient mixing procedure. A simple mass-balance-based model of a nutrient mixing tank with connections to different water sources, drains, and fertilizers is provided. Using methods of static optimization, a program was developed which, in consideration of various process constraints and optimization variables, is able to calculate the necessary steps to mix the desired solution. The program code is provided in an open-source repository. The flexibility of the method is demonstrated in simulation scenarios. The program is easy to use and to adapt, and all necessary steps are explained in this paper. This work contributes to a higher automation level in CEA.

Beyond Zipf’s law: Exploring the discrete generalized beta distribution in open-source repositories

Rank-size distributions, such as Zipf’s Law, have been instrumental in providing insights into the emergence of hierarchies across diverse systems, from linguistic corpuses to urban structures. However, the application of Zipf’s Law reveals limitations, particularly in its focus on distribution tails, sometimes overlooking a large proportion of the data which might play a pivotal role in system dynamics. Yet, fitting rank-size distributions other than a straight line on the log–log scale requires caution. In this study, we re-evaluate the utility of rank-size distributions by contrasting the traditional Zipf’s Law with the Discrete Generalized Beta Distribution (DGBD). We show the need of cautious fitting techniques for rank distributions, including the use of binning to prevent overfitting to data tails. Through both analytical derivation and empirical validation on commit data of open-source repositories, we show that DGBD consistently improves over Zipf distribution for concave rank distributions of large datasets (N≥100). This approach contributes to the advancement of methodologies for analyzing hierarchical systems.

OpenHands: An Open-Source Statistical Shape Model of the Finger Bones

This paper presents statistical shape models of the four fingers of the hand, with an emphasis on anatomic analysis of the proximal and distal interphalangeal joints. A multi-body statistical shape modelling pipeline was implemented on an exemplar training dataset of computed tomography (CT) scans of 10 right hands (5F:5M, 27–37 years, free from disease or injury) imaged at 0.3 mm resolution, segmented, meshed and aligned. Model generated included pose neutralisation to remove joint angle variation during imaging. Repositioning was successful; no joint flexion variation was observed in the resulting model. The first principal component (PC) of morphological variation represented phalanx size in all fingers. Subsequent PCs showed variation in position along the palmar-dorsal axis, and bone breadth: length ratio. Finally, the models were interrogated to provide gross measures of bone lengths and joint spaces. These models have been published for open use to support wider community efforts in hand biomechanical analysis, providing bony anatomy descriptions whilst preserving the security of the underlying imaging data and privacy of the participants. The model describes a small, homogeneous population, and assumptions cannot be made about how it represents individuals outside the training dataset. However, it supplements anthropometric datasets with additional shape information, and may be useful for investigating factors such as joint morphology and design of hand-interfacing devices and products. The model has been shared as an open-source repository (https://github.com/abel-research/OpenHands), and we encourage the community to use and contribute to it.

Ensemble of HMMs for Sequence Prediction on Multivariate Biomedical Data

Background: Biomedical data are usually collections of longitudinal data assessed at certain points in time. Clinical observations assess the presences and severity of symptoms, which are the basis for the description and modeling of disease progression. Deciphering potential underlying unknowns from the distinct observation would substantially improve the understanding of pathological cascades. Hidden Markov Models (HMMs) have been successfully applied to the processing of possibly noisy continuous signals. We apply ensembles of HMMs to categorically distributed multivariate time series data, leaving space for expert domain knowledge in the prediction process. Methods: We use an ensemble of HMMs to predict the loss of free walking ability as one major clinical deterioration in the most common autosomal dominantly inherited ataxia disorder worldwide. Results: We present a prediction pipeline that processes data paired with a configuration file, enabling us to train, validate and query an ensemble of HMMs. In particular, we provide a theoretical and practical framework for multivariate time-series inference based on HMMs that includes constructing multiple HMMs, each to predict a particular observable variable. Our analysis is conducted on pseudo-data, but also on biomedical data based on Spinocerebellar ataxia type 3 disease. Conclusions: We find that the model shows promising results for the data we tested. The strength of this approach is that HMMs are well understood, probabilistic and interpretable models, setting it apart from most Deep Learning approaches. We publish all code and evaluation pseudo-data in an open-source repository.

Automated Mapping of Vulnerability Advisories onto their Fix Commits in Open Source Repositories

The lack of comprehensive sources of accurate vulnerability data represents a critical obstacle to studying and understanding software vulnerabilities (and their corrections). In this article, we present an approach that combines heuristics stemming from practical experience and machine-learning (ML)—specifically, natural language processing (NLP)—to address this problem. Our method consists of three phases. First, we construct an advisory record object containing key information about a vulnerability that is extracted from an advisory, such as those found in the National Vulnerability Database (NVD). These advisories are expressed in natural language. Second, using heuristics, a subset of candidate fix commits is obtained from the source code repository of the affected project, by filtering out commits that can be identified as unrelated to the vulnerability at hand. Finally, for each of the remaining candidate commits, our method builds a numerical feature vector reflecting the characteristics of the commit that are relevant to predicting its match with the advisory at hand. Based on the values of these feature vectors, our method produces a ranked list of candidate fixing commits. The score attributed by the ML model to each feature is kept visible to the users, allowing them to easily interpret the predictions. We implemented our approach and we evaluated it on an open data set, built by manual curation, that comprises 2,391 known fix commits corresponding to 1,248 public vulnerability advisories. When considering the top-10 commits in the ranked results, our implementation could successfully identify at least one fix commit for up to 84.03% of the vulnerabilities (with a fix commit on the first position for 65.06% of the vulnerabilities). Our evaluation shows that our method can reduce considerably the manual effort needed to search open-source software (OSS) repositories for the commits that fix known vulnerabilities.

Mining for cost awareness in the infrastructure as code artifacts of cloud-based applications: An exploratory study

Context:Cloud computing’s rise as the primary platform for software development and delivery is largely driven by the potential cost savings. However, it is surprising that no empirical evidence has been collected to determine whether cost awareness permeates the development process and how it manifests in practice. Objective:This study aims to provide empirical evidence of cost awareness by mining open source repositories of cloud-based applications. The focus is on Infrastructure-as-Code artifacts that automate software (re)deployment on the cloud. Methods:A systematic examination of 152735 repositories yielded 2010 relevant hits. We then analyzed 538 relevant commits and 208 relevant issues using inductive and deductive coding and corroborated findings with discussions from Stack Overflow. Results:The findings indicate that developers are not only concerned with the cost of their application deployments but also take actions to reduce these costs beyond selecting cheaper cloud services. We also identify research areas for future consideration. Conclusion:Although we focus on a particular Infrastructure-as-Code technology (Terraform), the findings can be applicable to cloud-based application development in general. The provided empirical grounding can serve developers seeking to reduce costs through service selection, resource allocation, deployment optimization, and other techniques.

What Makes a Good TODO Comment?

Software development is a collaborative process that involves various interactions among individuals and teams. TODO comments in source code play a critical role in managing and coordinating diverse tasks during this process. However, this study finds that a large proportion of open-source project TODO comments are left unresolved or take a long time to be resolved. About 46.7% of TODO comments in open-source repositories are of low-quality (e.g., TODOs that are ambiguous, lack information, or are useless to developers). This highlights the need for better TODO practices. In this study, we investigate four aspects regarding the quality of TODO comments in open-source projects: (1) the prevalence of low-quality TODO comments; (2) the key characteristics of high-quality TODO comments; (3) how are TODO comments of different quality managed in practice; and (4) the feasibility of automatically assessing TODO comment quality. Examining 2,863 TODO comments from Top100 GitHub Java repositories, we propose criteria to identify high-quality TODO comments and provide insights into their optimal composition. We discuss the lifecycle of TODO comments with varying quality. To assist developers, we construct deep learning-based methods that show promising performance in identifying the quality of TODO comments, potentially enhancing development efficiency and code quality.

Code search engines for the next generation

Given the abundance of software in open source repositories, code search engines are increasingly turning to “big data” technologies such as natural language processing and machine learning, to deliver more useful search results. However, like the syntax-based approaches traditionally used to analyze and compare code in the first generation of code search engines, big data technologies are essentially static analysis processes. When dynamic properties of software, such as run-time behavior (i.e., semantics) and performance, are among the search criteria, the exclusive use of static algorithms has a significant negative impact on the precision and recall of the search results as well as other key usability factors such as ranking quality. Therefore, to address these weaknesses and provide a more reliable and usable service, the next generation of code search engines needs to complement static code analysis techniques with equally large-scale, dynamic analysis techniques based on its execution and observation. In this paper we describe a new software platform specifically developed to achieve this by simplifying and largely automating the dynamic analysis (i.e., observation) of code at a large scale. We show how this platform can combine dynamically observed properties of code modules with static properties to improve the quality and usability of code search results.

Measuring and Clustering Heterogeneous Chatbot Designs

Conversational agents, or chatbots, have become popular to access all kind of software services. They provide an intuitive natural language interface for interaction, available from a wide range of channels including social networks, web pages, intelligent speakers or cars. In response to this demand, many chatbot development platforms and tools have emerged. However, they typically lack support to statically measure properties of the chatbots being built, as indicators of their size, complexity, quality or usability. Similarly, there are hardly any mechanisms to compare and cluster chatbots developed with heterogeneous technologies. To overcome this limitation, we propose a suite of 21 metrics for chatbot designs, as well as two clustering methods that help in grouping chatbots along their conversation topics and design features. Both the metrics and the clustering methods are defined on a neutral chatbot design language, becoming independent of the implementation platform. We provide automatic translations of chatbots defined on some major platforms into this neutral notation to perform the measurement and clustering. The approach is supported by our tool Asymob , which we have used to evaluate the metrics and the clustering methods over a set of 259 Dialogflow and Rasa chatbots from open-source repositories. The results open the door to incorporating the metrics within chatbot development processes for the early detection of quality issues, and to exploit clustering to organise large collections of chatbots into significant groups to ease chatbot comprehension, search and comparison.

Analysis and Evaluation of a Searchable Exercise Repository for Training Java Programming

This paper presents the first experiences of the use of an online open-source repository with programming exercises. The repository is independent of any specific teaching approach. Students can search for and select an exercise that trains the programming concepts that they want to train and that only uses the programming concepts they already know. Then, they can submit their solutions and get automatic feedback from the system. We analyzed quantitatively how students used the system by inspecting the logged actions of the students using the system. We also did a qualitative analysis by interviews, to find out how the students appreciated the use of the repository and to get feedback for improvements. We focused on how students select exercises as finding the exercise that fulfills the training needs of a student is the innovative part of our repository.

Mapping APIs in Dynamic-typed Programs by Leveraging Transfer Learning

Application Programming Interface (API) migration is a common task for adapting software across different programming languages and platforms, where manually constructing the mapping relations between APIs is indeed time-consuming and error-prone. To facilitate this process, many automated API mapping approaches have been proposed. However, existing approaches were mainly designed and evaluated for mapping APIs of statically-typed languages, while their performance on dynamically-typed languages remains unexplored. In this paper, we conduct the first extensive study to explore existing API mapping approaches’ performance for mapping APIs in dynamically-typed languages, for which we have manually constructed a high-quality dataset. According to the empirical results, we have summarized several insights. In particular, the source code implementations of APIs can significantly improve the effectiveness of API mapping. However, due to the confidentiality policy, they may not be available in practice. To overcome this, we propose a novel API mapping approach, named Matl , which leverages the transfer learning technique to learn the semantic embeddings of source code implementations from large-scale open-source repositories and then transfers the learned model to facilitate the mapping of APIs. In this way, Matl can produce more accurate API embedding of its functionality for more effective mapping without knowing the source code of the APIs. To evaluate the performance of Matl , we have conducted an extensive study by comparing Matl with state-of-the-art approaches. The results demonstrate that Matl is indeed effective as it improves the state-of-the-art approach by at least 18.36% for mapping APIs of dynamically-typed language and by 30.77% for mapping APIs of the statically-typed language.

Refractiveindex.info database of optical constants

We introduce the refractiveindex.info database, a comprehensive open-source repository containing optical constants for a wide array of materials, and describe in detail the underlying dataset. This collection, derived from a meticulous compilation of data sourced from peer-reviewed publications, manufacturers’ datasheets, and authoritative texts, aims to advance research in optics and photonics. The data is stored using a YAML-based format, ensuring integrity, consistency, and ease of access. Each record is accompanied by detailed metadata, facilitating a comprehensive understanding and efficient utilization of the data. In this descriptor, we outline the data curation protocols and the file format used for data records, and briefly demonstrate how the data can be organized in a user-friendly fashion akin to the books in a traditional library.

The Pelagic Species Trait Database, an open data resource to support trait-based ocean research

Trait-based frameworks are increasingly used for predicting how ecological communities respond to ongoing global change. As species range shifts result in novel encounters between predators and prey, identifying prey ‘guilds’, based on a suite of shared traits, can distill complex species interactions, and aid in predicting food web dynamics. To support advances in trait-based research in open-ocean systems, we present the Pelagic Species Trait Database, an extensive resource documenting functional traits of 529 pelagic fish and invertebrate species in a single, open-source repository. We synthesized literature sources and online resources, conducted morphometric analysis of species images, as well as laboratory analyses of trawl-captured specimens to collate traits describing 1) habitat use and behavior, 2) morphology, 3) nutritional quality, and 4) population status information. Species in the dataset primarily inhabit the California Current system and broader NE Pacific Ocean, but also includes pelagic species known to be consumed by top ocean predators from other ocean basins. The aim of this dataset is to enhance the use of trait-based approaches in marine ecosystems and for predator populations worldwide.

A Low-cost 4K Video Streaming Underwater Observatory for Coastal Oceans

This paper presents an open-source underwater observatory for coastal data acquisition. The observatory combines a 4K underwater camera, an acoustic hydrophone, and auxiliary water quality sensors, linked by a tethered connection to a surface hub for remote data transmission. Due to the accumulation of microorganisms (biofouling) during long-time deployments, a magnetic wiper mechanism is integrated into the camera housing to clear the view. An open-source repository of the underwater observatory is made publicly available: https://github.com/OsloMet-OceanLab/aneris

TransCNNLoc: End-to-end pixel-level learning for 2D-to-3D pose estimation in dynamic indoor scenes

Accurate localization in GPS-denied environments has always been a core issue in computer vision and robotics research. In indoor environments, vision-based localization methods are susceptible to changes in lighting conditions, viewing angles, and environmental factors, resulting in localization failures or limited generalization capabilities. In this paper, we propose the TransCNNLoc framework, which consists of an encoding–decoding network designed to learn more robust image features for camera pose estimation. In the image feature encoding stage, CNN and Swin Transformer are integrated to construct the image feature encoding module, enabling the network to fully extract global context and local features from images. In the decoding stage, multi-level image features are decoded through cross-layer connections while computing per-pixel feature weight maps. To enhance the framework’s robustness to dynamic objects, a dynamic object recognition network is introduced to optimize the feature weights. Finally, a multi-level iterative optimization from coarse to fine levels is performed to recover six degrees of freedom camera pose. Experiments were conducted on the publicly available 7scenes dataset as well as a dataset collected under changing lighting conditions and dynamic scenes for accuracy validation and analysis. The experimental results demonstrate that the proposed TransCNNLoc framework exhibits superior adaptability to dynamic scenes and lighting changes. In the context of static environments within publicly available datasets, the localization technique introduced in this study attains a maximal precision of up to 5 centimeters, consistently achieving superior outcomes across a majority of the scenarios. Under the conditions of dynamic scenes and fluctuating illumination, this approach demonstrates an enhanced precision capability, reaching up to 3 centimeters. This represents a substantial refinement from the decimeter scale to a centimeter scale in precision, marking a significant advancement over the existing state-of-the-art (SOTA) algorithms. The open-source repository for the method proposed in this paper can be found at the following URL: github.com/Geelooo/TransCNNloc.

License recommendation for open source projects in the power industry

Context:Establishing secure and appropriate licensing procedures for open-source software is essential in the development of a decentralized renewable energy system within the smart grid industry. Nonetheless, software developers in the power industry encounter obstacles in comprehending and electing licenses on account of factors such as resemblances in terms, intricacies of the law, compatibility of licenses, and the slow development of the open source movement in the power industry. Objective:This paper aims to comprehensively examine the licenses of open source projects in the power industry, which is essential for the completion and popularity of projects. A novel framework consisting of two stages (i.e. data processing and recommendation) is proposed to analyze the current situation of open source license selection in the power industry. Method:By analyzing 274,442 open source repositories related to 40 electricity-related keywords from GitHub, we developed a machine learning-powered license recommendation methodology. We first employed the K-means method to cluster the selected repositories and identified 6 major clusters. Next, we utilized the random forest method to predict licenses for new repositories based on the clustering results. We evaluated the accuracy of the model by testing it on training and testing datasets and achieved 96% accuracy. Results:We found that open source repository clusters in the power industry have distinct licensing preferences reflecting their unique objectives, with MIT being the most popular due to its permissiveness, and GPL-3.0, Apache-2.0, and BSD-3-Clause being favored by clusters valuing copyleft principles, closed-source derivatives protection, and control over software use, respectively. In addition, the study recognizes the content of open source projects as a meaningful indicator for license recommendation. Conclusion:These insights substantially enhance comprehension of the distribution and the selection of open source licenses in the power industry, potentially aiding future research on license recommendation in this field.

LidarBoX: a 3D-printed, open-source altimeter system to improve photogrammetric accuracy for off-the-shelf drones

Drones provide a privileged birds’-eye view for collecting high-resolution imagery for morphometric and behavioral sampling of animals. Biologically meaningful measurements extracted from overhead images require an accurate estimate of altitude, but current commercial drones include inaccurate barometer estimates. Recent proposals for coupling altimeter systems to drones have provided customized, open-source solutions, yet assembling such altimeter systems requires advanced technical skills, thereby potentially limiting their use. Here, we built upon recent advances to provide a 3D-printed enclosure for an altimeter system that is inexpensive, self-contained, easy to setup, and transferable across commercial drones. We depart from a published, successful data logger system composed of a GPS and LiDAR sensor and design a more compact and self-powered version (“LidarBoX”) that easily attaches to a variety of commercial drones. We compare flight times with/without LidarBoX attached, test flight maneuverability and performance, and validate the reliability of measurement accuracy. To make LidarBoX accessible, we provide an open-source repository with design code and files and a how-to-assemble guide for non-specialists. We hope this work helps popularize LiDAR altimeter systems on commercial drones to improve the accuracy and reliability of drones as a sampling platform for ecology and wildlife research.