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1. Patterns of Effort Contribution and Demand and User Classification based on Participation Patterns in NPM Ecosystem

   Dey, Tapajit; Ma, Yuxing; Mockus, Audris (September 2019, In Proceedings of the 15th International Conference on Predictive Models and Data Analytics in Software Engineering)

   Background: Open source requires participation of volunteer and commercial developers (users) in order to deliver functional high-quality components. Developers both contribute effort in the form of patches and demand effort from the component maintainers to resolve issues reported against it. Open source components depend on each other directly and transitively, and evidence suggests that more effort is required for reporting and resolving the issues reported further upstream in this supply chain. Aim: Identify and characterize patterns of effort contribution and demand throughout the open source supply chain and investigate if and how these patterns vary with developer activity; identify different groups of developers; and predict developers' company affiliation based on their participation patterns. Method: 1,376,946 issues and pull-requests created for 4433 NPM packages with over 10,000 monthly downloads and full (public) commit activity data of the 272,142 issue creators is obtained and analyzed and dependencies on NPM packages are identified. Fuzzy c-means clustering algorithm is used to find the groups among the users based on their effort contribution and demand patterns, and Random Forest is used as the predictive modeling technique to identify their company affiliations. Result: Users contribute and demand effort primarily from packages that they depend on directly with only a tiny fraction of contributions and demand going to transitive dependencies. A significant portion of demand goes into packages outside the users' respective supply chains (constructed based on publicly visible version control data). Three and two different groups of users are observed based on the effort demand and effort contribution patterns respectively. The Random Forest model used for identifying the company affiliation of the users gives a AUC-ROC value of 0.68, and variables representing aggregate participation patterns proved to be the important predictors. Conclusion: Our results give new insights into effort demand and supply at different parts of the supply chain of the NPM ecosystem and its users and suggests the need to increase visibility further upstream. 

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2. Patterns of Effort Contribution and Demand and User Classification based on Participation Patterns in NPM Ecosystem

   https://doi.org/10.1145/3345629.3345634  

   Dey, Tapajit; Ma, Yuxing; Mockus, Audris (August 2019, PROMISE'19: Proceedings of the Fifteenth International Conference on Predictive Models and Data Analytics in Software Engineering)

   Background: Open source requires participation of volunteer and commercial developers (users) in order to deliver functional high-quality components. Developers both contribute effort in the form of patches and demand effort from the component maintainers to resolve issues reported against it. Open source components depend on each other directly and transitively, and evidence suggests that more effort is required for reporting and resolving the issues reported further upstream in this supply chain. Aim: Identify and characterize patterns of effort contribution and demand throughout the open source supply chain and investigate if and how these patterns vary with developer activity; identify different groups of developers; and predict developers' company affiliation based on their participation patterns. Method: 1,376,946 issues and pull-requests created for 4433 NPM packages with over 10,000 monthly downloads and full (public) commit activity data of the 272,142 issue creators is obtained and analyzed and dependencies on NPM packages are identified. Fuzzy c-means clustering algorithm is used to find the groups among the users based on their effort contribution and demand patterns, and Random Forest is used as the predictive modeling technique to identify their company affiliations. Result: Users contribute and demand effort primarily from packages that they depend on directly with only a tiny fraction of contributions and demand going to transitive dependencies. A significant portion of demand goes into packages outside the users' respective supply chains (constructed based on publicly visible version control data). Three and two different groups of users are observed based on the effort demand and effort contribution patterns respectively. The Random Forest model used for identifying the company affiliation of the users gives a AUC-ROC value of 0.68, and variables representing aggregate participation patterns proved to be the important predictors. Conclusion: Our results give new insights into effort demand and supply at different parts of the supply chain of the NPM ecosystem and its users and suggests the need to increase visibility further 

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3. GitHub Actions: The Impact on the Pull Request Process

   https://doi.org/10.1007/s10664-023-10369-w  

   Wessel, Mairieli; Vargovich, Joseph; Gerosa, Marco_A; Treude, Christoph (September 2023, Empirical Software Engineering)

   Abstract Software projects frequently use automation tools to perform repetitive activities in the distributed software development process. Recently, GitHub introducedGitHub Actions, a feature providing automated workflows for software projects. Understanding and anticipating the effects of adopting such technology is important for planning and management. Our research investigates how projects useGitHub Actions, what the developers discuss about them, and how project activity indicators change after their adoption. Our results indicate that 1,489 out of 5,000 most popular repositories (almost 30% of our sample) adoptGitHub Actionsand that developers frequently ask for help implementing them. Our findings also suggest that the adoption ofGitHub Actionsleads to more rejections of pull requests (PRs), more communication in accepted PRs and less communication in rejected PRs, fewer commits in accepted PRs and more commits in rejected PRs, and more time to accept a PR. We found similar results when segmenting our results by categories ofGitHub Actions. We suggest practitioners consider these effects when adoptingGitHub Actionson their projects. 

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4. Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs

   https://doi.org/10.1038/s41467-023-39631-x  

   Pyrros, Ayis; Borstelmann, Stephen M; Mantravadi, Ramana; Zaiman, Zachary; Thomas, Kaesha; Price, Brandon; Greenstein, Eugene; Siddiqui, Nasir; Willis, Melinda; Shulhan, Ihar; et al (December 2023, Nature Communications)

   Abstract Deep learning (DL) models can harness electronic health records (EHRs) to predict diseases and extract radiologic findings for diagnosis. With ambulatory chest radiographs (CXRs) frequently ordered, we investigated detecting type 2 diabetes (T2D) by combining radiographic and EHR data using a DL model. Our model, developed from 271,065 CXRs and 160,244 patients, was tested on a prospective dataset of 9,943 CXRs. Here we show the model effectively detected T2D with a ROC AUC of 0.84 and a 16% prevalence. The algorithm flagged 1,381 cases (14%) as suspicious for T2D. External validation at a distinct institution yielded a ROC AUC of 0.77, with 5% of patients subsequently diagnosed with T2D. Explainable AI techniques revealed correlations between specific adiposity measures and high predictivity, suggesting CXRs’ potential for enhanced T2D screening. 

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5. Exploring Gut Microbiome in Predicting the Efficacy of Immunotherapy in Non-Small Cell Lung Cancer

   https://doi.org/10.3390/cancers14215401  

   Liu, Ben; Chau, Justin; Dai, Qun; Zhong, Cuncong; Zhang, Jun (November 2022, Cancers)

   We performed various analyses on the taxonomic and functional features of the gut microbiome from NSCLC patients treated with immunotherapy to establish a model that may predict whether a patient will benefit from immunotherapy. We collected 65 published whole metagenome shotgun sequencing samples along with 14 samples from our previous study. We systematically studied the taxonomical characteristics of the dataset and used both the random forest (RF) and the multilayer perceptron (MLP) neural network models to predict patients with progression-free survival (PFS) above 6 months versus those below 3 months. Our results showed that the RF classifier achieved the highest F-score (85.2%) and the area under the receiver operating characteristic curve (AUC) (95%) using the protein families (Pfam) profile, and the MLP neural network classifier achieved a 99.9% F-score and 100% AUC using the same Pfam profile. When applying the model trained in the Pfam profile directly to predict the treatment response, we found that both trained RF and MLP classifiers significantly outperformed the stochastic predictor in F-score. Our results suggested that such a predictive model based on functional (e.g., Pfam) rather than taxonomic profile might be clinically useful to predict whether an NSCLC patient will benefit from immunotherapy, as both the F-score and AUC of functional profile outperform that of taxonomic profile. In addition, our model suggested that interactive biological processes such as methanogenesis, one-carbon, and amino acid metabolism might be important in regulating the immunotherapy response that warrants further investigation. 

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