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Predicting health indicators for open source projects (using hyperparameter optimization)


Software developed on public platform is a source of data that can be used to make predictions about those projects. While the individual developing activity may be random and hard to predict, the developing behavior on project level can be predicted with good accuracy when large groups of developers work together on software projects. To demonstrate this, we use 64,181 months of data from 1,159 GitHub projects to make various predictions about the recent status of those projects (as of April 2020). We find that traditional estimation algorithms make many mistakes. Algorithms like k-nearest neighbors (KNN), support vector regression (SVR), random forest (RFT), linear regression (LNR), and regression trees (CART) have high error rates. But that error rate can be greatly reduced using hyperparameter optimization. To the best of our knowledge, this is the largest study yet conducted, using recent data for predicting multiple health indicators of open-source projects. To facilitate open science (and replications and extensions of this work), all our materials are available online at

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  1. In a recent TSE’21 article. we have explained by SE hyperparameter optimization can be so simple: SE data can be intrinsically simpler than other kinds of data and, hence simpler to explore (see Figure 6d of Agrawal et al. (2021)).


  3. We use default settings for the baselines to find if they can provide good prediction performance, and how much space hyperparameter-tuning can improve. Using a pre-selected parameter-settings from literature may bring bias because of different data format or prediction tasks.

  4. i.e. A maximum of 200 evaluations for Random Search, Grid Search, Flash and DE; for ASKL, maximum runtime for each project is restricted to 15 seconds, please see Section 5.1 for details.

  5. In the Apache Software Foundation, projects can be canceled and “moved to the attic” ( when they are unable to muster 3 votes for a release, lack of active contributors, or unable to fulfill their reporting duties to the Foundation.


  • Aggarwal K, Hindle A, Stroulia E (2014) Co-evolution of project documentation and popularity within github. In: Proceedings of the 11th working conference on mining software repositories, pp 360–363

  • Agrawal A, Fu W, Chen D, Shen X, Menzies T (2019) How to” DODGE” complex software analytics. IEEE Trans Softw Eng

  • Agrawal A, Menzies T (2018) Is” better data” better than” better data miners”?. In: 2018 IEEE/ACM 40th international conference on software engineering (ICSE), IEEE, pp 1050–1061

  • Agrawal A, Menzies T, Minku LL, Wagner M, Yu Z (2018) Better software analytics via” DUO”: Data mining algorithms using/used-by optimizers. arXiv:1812.01550

  • Agrawal A, Yang X, Agrawal R, Yedida R, Shen X, Menzies T (2021) Simpler hyperparameter optimization for software analytics: Why, how, when. IEEE Trans Softw Eng, 1–1.

  • Bao L, Xia X, Lo D, Murphy GC (2019) A large scale study of long-time contributor prediction for github projects. IEEE Trans Softw Eng

  • Bergstra JS, Bardenet R, Bengio Y, Kégl B (2011) Algorithms for hyper-parameter optimization. In: Advances in neural information processing systems, pp 2546–2554

  • Bidoki NH, Sukthankar G, Keathley H, Garibay I (2018) A cross-repository model for predicting popularity in github. In: 2018 international conference on computational science and computational intelligence (CSCI), IEEE, pp 1248–1253

  • Borges H, Hora A, Valente MT (2016a) Predicting the popularity of github repositories. In: Proceedings of the The 12th international conference on predictive models and data analytics in software engineering, pp 1–10

  • Borges H, Hora A, Valente MT (2016b) Understanding the factors that impact the popularity of github repositories. In: 2016 IEEE international conference on software maintenance and evolution (ICSME), IEEE, pp 334–344

  • C M, MacDonell S (2012) Evaluating prediction systems in software project estimation. IST 54(8):820–827

    Google Scholar 

  • Chen C, Twycross J, Garibaldi JM (2017) A new accuracy measure based on bounded relative error for time series forecasting. PloS One 12:3

    Google Scholar 

  • Chen F, Li L, Jiang J, Zhang L (2014) Predicting the number of forks for open source software project. In: Proceedings of the 2014 3rd International workshop on evidential assessment of software technologies, pp 40–47

  • Coelho J, Valente M T, Milen L, Silva L L (2020) Is this github project maintained? measuring the level of maintenance activity of open-source projects. Information and Software Technology 122

  • Cohen PR (1995) Empirical methods for artificial intelligence. MIT Press, Cambridge, MA, USA

    MATH  Google Scholar 

  • Crowston K, Howison J (2006) Assessing the health of open source communities. Computer 39(5):89–91

    Article  Google Scholar 

  • Das S, Mullick S S, Suganthan P N (2016) Recent advances in differential evolution–an updated survey. Swarm and Evolutionary Computation 27:1–30

    Article  Google Scholar 

  • Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research 7:1–30

    MathSciNet  MATH  Google Scholar 

  • Feldt R, Magazinius A (2010) Validity threats in empirical software engineering research-an initial survey. In: SEKE, pp 374–379

  • Feurer M, Klein A, Eggensperger K, Springenberg J T, Blum M, Hutter F (2019) Auto-sklearn: Efficient and robust automated machine learning. In: Automated Machine Learning. Springer, Cham, pp 113–134

  • Foss T, Stensrud E, Kitchenham B, Myrtveit I (2003) A simulation study of the model evaluation criterion mmre. TSE 29(11):985–995

    Google Scholar 

  • Foundation A S (2018) Apache software foundation projects

  • Foundation L (2020) Community health analytics open source software

  • Foundation L (2020) Linux foundation projects

  • Friedman M (1940) A comparison of alternative tests of significance for the problem of m rankings. The Annals of Mathematical Statistics 11(1):86–92

    MathSciNet  Article  Google Scholar 

  • Fu W, Menzies T, Shen X (2016) Tuning for software analytics: Is it really necessary?. IST Journal 76:135–146

    Google Scholar 

  • Fu W, Nair V, Menzies T (2016) Why is differential evolution better than grid search for tuning defect predictors?. arXiv:1609.02613

  • Georg JPL, Germonprez M (2018) Assessing open source project health

  • Han J, Deng S, Xia X, Wang D, Yin J (2019) Characterization and prediction of popular projects on github. In: 2019 IEEE 43rd annual computer software and applications conference (COMPSAC), IEEE, vol 1, pp 21–26

  • Herbold S (2017) Comments on scottknottesd in response to” an empirical comparison of model validation techniques for defect prediction models”. IEEE Trans Softw Eng 43(11):1091–1094

    Article  Google Scholar 

  • Herbold S, Trautsch A, Grabowski J (2018) Correction of “A comparative study to benchmark cross-project defect prediction approaches”. IEEE Trans Softw Eng 45(6):632–636

    Article  Google Scholar 

  • Hohl P, Stupperich M, Münch J, Schneider K (2018) An assessment model to foster the adoption of agile software product lines in the automotive domain. In: 2018 IEEE international conference on engineering, technology and innovation (ICE/ITMC), IEEE, pp 1–9

  • Jansen S (2014) Measuring the health of open source software ecosystems: Beyond the scope of project health. Inf Softw Technol 56(11):1508–1519

    Article  Google Scholar 

  • Jarczyk O, Jaroszewicz S, Wierzbicki A, Pawlak K, Jankowski-Lorek M (2018) Surgical teams on github: Modeling performance of github project development processes. Inf Softw Technol 100:32–46

    Article  Google Scholar 

  • Kalliamvakou E, Gousios G, Blincoe K, Singer L, German D M, Damian D (2014) The promises and perils of mining github. In: Proceedings of the 11th working conference on mining software repositories, pp 92–101

  • Kalliamvakou E, Gousios G, Blincoe K, Singer L, German D M, Damian D (2016) An in-depth study of the promises and perils of mining github. Empir Softw Eng 21(5):2035–2071

    Article  Google Scholar 

  • Kikas R, Dumas M, Pfahl D (2016) Using dynamic and contextual features to predict issue lifetime in github projects. In: 2016 IEEE/ACM 13th working conference on mining software repositories (MSR), IEEE, pp 291–302

  • Kitchenham B A, Pickard L M, MacDonell S G, Shepperd M J (2001) What accuracy statistics really measure. IEEE Softw 148(3):81–85

    Article  Google Scholar 

  • Korte M, Port D (2008) Confidence in software cost estimation results based on mmre and pred. In: PROMISE’08, pp 63–70

  • Krishna R, Agrawal A, Rahman A, Sobran A, Menzies T (2018) What is the connection between issues, bugs, and enhancements?. In: 2018 IEEE/ACM 40th international conference on software engineering: software engineering in practice track (ICSE-SEIP), IEEE, pp 306–315

  • Krishna R, Nair V, Jamshidi P, Menzies T (2021) Whence to learn? transferring knowledge in configurable systems using BEETLE. IEEE Trans Softw Eng 47(12):2956–2972.

    Article  Google Scholar 

  • Langdon W B, Dolado J, Sarro F, Harman M (2016) Exact mean absolute error of baseline predictor, MARP0. IST 73:16–18

    Google Scholar 

  • Liao Z, Yi M, Wang Y, Liu S, Liu H, Zhang Y, Zhou Y (2019) Healthy or not: A way to predict ecosystem health in github. Symmetry 11(2):144

    Article  Google Scholar 

  • Manikas K, Hansen K M (2013) Reviewing the health of software ecosystems-a conceptual framework proposal. In: Proceedings of the 5th international workshop on software ecosystems (IWSECO), Citeseer, pp 33–44

  • Minku L L (2019) A novel online supervised hyperparameter tuning procedure applied to cross-company software effort estimation. Empir Softw Eng 24 (5):3153–3204

    Article  Google Scholar 

  • Molokken K, Jorgensen M (2003) A review of software surveys on software effort estimation. In: Empirical Software Engineering, 2003. ISESE 2003. Proceedings. 2003 International Symposium on, IEEE, pp 223–230

  • Molokken K, Jorgensen M (2003) A review of software surveys on software effort estimation. In: 2003 International Symposium on Empirical Software Engineering, 2003. ISESE 2003. Proceedings, IEEE, pp 223–230

  • Munaiah N, Kroh S, Cabrey C, Nagappan M (2017) Curating github for engineered software projects. Empir Softw Eng 22(6):3219–3253

    Article  Google Scholar 

  • Nagy A, Njima M, Mkrtchyan L (2010) A bayesian based method for agile software development release planning and project health monitoring. In: 2010 international conference on intelligent networking and collaborative systems, IEEE, pp 192–199

  • Nair V, Yu Z, Menzies T, Siegmund N, Apel S (2018) Finding faster configurations using flash. IEEE Transactions on Software Engineering 1–1.

  • Nemenyi PB (1963) Distribution-free multiple comparisons. Princeton University

  • Paasivaara M, Behm B, Lassenius C, Hallikainen M (2018) Large-scale agile transformation at ericsson: a case study. Empir Softw Eng 23(5):2550–2596

    Article  Google Scholar 

  • Parnin C, Helms E, Atlee C, Boughton H, Ghattas M, Glover A, Holman J, Micco J, Murphy B, Savor T et al (2017) The top 10 adages in continuous deployment. IEEE Softw 34(3):86–95

    Article  Google Scholar 

  • Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: Machine learning in python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  • Port D, Korte M (2008) Comparative studies of the model evaluation criterion mmre and pred in software cost estimation research. In: ESEM’08, pp 51–60

  • Qi F, Jing X-Y, Zhu X, Xie X, Xu B, Ying S (2017) Software effort estimation based on open source projects: Case study of github. Inf Softw Technol 92:145–157

    Article  Google Scholar 

  • Santos A R, Kroll J, Sales A, Fernandes P, Wildt D (2016) Investigating the adoption of agile practices in mobile application development. In: ICEIS (1), pp 490–497

  • Sarro F, Petrozziello A, Harman M (2016) Multi-objective software effort estimation. In: ICSE, ACM, pp 619–630

  • Shepperd M, Cartwright M, Kadoda G (2000) On building prediction systems for software engineers. EMSE 5(3):175–182

    MATH  Google Scholar 

  • Shrikanth NC, Menzies T (2021) The early bird catches the worm: Better early life cycle defect predictors. arXiv:2105.11082

  • Snoek J, Larochelle H, Adams R P (2012) Practical bayesian optimization of machine learning algorithms. arXiv:1206.2944

  • Stensrud E, Foss T, Kitchenham B, Myrtveit I (2003) A further empirical investigation of the relationship of mre and project size. ESE 8(2):139–161

    Google Scholar 

  • Stewart K (2019) Personnel communication

  • Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over cont. spaces. JoGO 11(4):341–359

    MATH  Google Scholar 

  • Tantithamthavorn C, McIntosh S, Hassan A E, Matsumoto K (2016) Automated parameter optimization of classification techniques for defect prediction models. In: Proceedings of the 38th international conference on software engineering, pp 321–332

  • Tantithamthavorn C, McIntosh S, Hassan A E, Matsumoto K (2018) The impact of automated parameter optimization on defect prediction models. IEEE Trans Softw Eng 45(7):683–711

    Article  Google Scholar 

  • Tu H, Menzies T (2021) Frugal: Unlocking ssl for software analytics

  • Tu H, Papadimitriou G, Kiran M, Wang C, Mandal A, Deelman E, Menzies T (2021) Mining workflows for anomalous data transfers. In: 2021 IEEE/ACM 18th international conference on mining software repositories (MSR), pp 1–12

  • Wahyudin D, Mustofa K, Schatten A, Biffl S, Tjoa A M (2007) Monitoring the “health” status of open source web-engineering projects. International Journal of Web Information Systems

  • Wang T, Zhang Y, Yin G, Yu Y, Wang H (2018) Who will become a long-term contributor? a prediction model based on the early phase behaviors. In: Proceedings of the Tenth Asia-Pacific symposium on internetware, pp 1–10

  • Weber S, Luo J (2014) What makes an open source code popular on git hub?. In: 2014 IEEE international conference on data mining workshop, IEEE, pp 851–855

  • Witten I H, Frank E, Hall M A (2011) Data mining: Practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA

    Google Scholar 

  • Wu G, Shen X, Li H, Chen H, Lin A, Suganthan P N (2018) Ensemble of differential evolution variants. Inf Sci 423:172–186

    MathSciNet  Article  Google Scholar 

  • Wynn Jr D (2007) Assessing the health of an open source ecosystem. In: Emerging Free and Open Source Software Practices. IGI Global, pp 238–258

  • Xia T (2021) Principles of project health for open source software

  • Xia T, Shu R, Shen X, Menzies T (2020) Sequential model optimization for software effort estimation. IEEE Transactions on Software Engineering

  • Yu Y, Wang H, Yin G, Wang T (2016) Reviewer recommendation for pull-requests in github: What can we learn from code review and bug assignment?. Inf Softw Technol 74:204–218

    Article  Google Scholar 

  • Zemlin J (2017) If you can’t measure it, you can’t improve it.

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This work is partially funded by a National Science Foundation Grant #1703487.

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Correspondence to Tim Menzies.

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Communicated by: Federica Sarro

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Xia, T., Fu, W., Shu, R. et al. Predicting health indicators for open source projects (using hyperparameter optimization). Empir Software Eng 27, 122 (2022).

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  • Hyperparameter optimization
  • Project health
  • Machine learning