Search Results - Nachiappan Nagappan

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  1. 21
    Searching for a Needle in a Haystack: Predicting Security Vulnerabilities for Windows Vista

    Searching for a Needle in a Haystack: Predicting Security Vulnerabilities for Windows Vista by Zimmermann, Thomas, Nagappan, Nachiappan, Williams, Laurie

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  2. 22
    ConE: A Concurrent Edit Detection Tool for Large-scale Software Development

    ConE: A Concurrent Edit Detection Tool for Large-scale Software Development by Maddila, Chandra, Nagappan, Nachiappan, Bird, Christian, Gousios, Georgios, van Deursen, Arie

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  3. 23
    CODEMINE: Building a Software Development Data Analytics Platform at Microsoft

    CODEMINE: Building a Software Development Data Analytics Platform at Microsoft by Czerwonka, J., Nagappan, N., Schulte, W., Murphy, B.

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  4. 24
    A Deep Dive into the Impact of COVID-19 on Software Development

    A Deep Dive into the Impact of COVID-19 on Software Development by da Mota Silveira Neto, Paulo Anselmo, Mannan, Umme Ayda, de Almeida, Eduardo Santana, Nagappan, Nachiappan, Lo, David, Singh Kochhar, Pavneet, Gao, Cuiyun, Ahmed, Iftekhar

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  5. 25
    Use of relative code churn measures to predict system defect density

    Use of relative code churn measures to predict system defect density by Nagappan, N., Ball, T.

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  6. 26
    DEVIATE: A Deep Learning Variance Testing Framework

    DEVIATE: A Deep Learning Variance Testing Framework by Pham, Hung Viet, Kim, Mijung, Tan, Lin, Yu, Yaoliang, Nagappan, Nachiappan

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  7. 27
    Do topics make sense to managers and developers?

    Do topics make sense to managers and developers? by Hindle, Abram, Bird, Christian, Zimmermann, Thomas, Nagappan, Nachiappan

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  8. 28
    Feedback-driven semi-supervised synthesis of program transformations

    Feedback-driven semi-supervised synthesis of program transformations by Gao, Xiang, Barke, Shraddha, Radhakrishna, Arjun, Soares, Gustavo, Gulwani, Sumit, Leung, Alan, Nagappan, Nachiappan, Tiwari, Ashish

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  9. 29
    Automatic Unit Test Generation for Machine Learning Libraries: How Far Are We?

    Automatic Unit Test Generation for Machine Learning Libraries: How Far Are We? by Wang, Song, Shrestha, Nishtha, Subburaman, Abarna Kucheri, Wang, Junjie, Wei, Moshi, Nagappan, Nachiappan

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  10. 30
    Can Program Synthesis be Used to Learn Merge Conflict Resolutions? An Empirical Analysis

    Can Program Synthesis be Used to Learn Merge Conflict Resolutions? An Empirical Analysis by Pan, Rangeet, Le, Vu, Nagappan, Nachiappan, Gulwani, Sumit, Lahiri, Shuvendu, Kaufman, Mike

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  11. 31
    Scoping Software Engineering for AI: The TSE Perspective

    Scoping Software Engineering for AI: The TSE Perspective by Uchitel, Sebastian, Chechik, Marsha, Penta, Massimiliano Di, Adams, Bram, Aguirre, Nazareno, Bavota, Gabriele, Bianculli, Domenico, Blincoe, Kelly, Cavalcanti, Ana, Dittrich, Yvonne, Ferrucci, Filomena, Hoda, Rashina, Huang, LiGuo, Lo, David, Lyu, Michael R., Ma, Lei, Maletic, Jonathan I., Mariani, Leonardo, McMillan, Collin, Menzies, Tim, Monperrus, Martin, Moreno, Ana, Nagappan, Nachiappan, Pasquale, Liliana, Pelliccione, Patrizio, Pradel, Michael, Purandare, Rahul, Ryu, Sukyoung, Sabetzadeh, Mehrdad, Serebrenik, Alexander, Sun, Jun, Tantithamthavorn, Kla, Treude, Christoph, Wimmer, Manuel, Xiong, Yingfei, Yue, Tao, Zaidman, Andy, Zhang, Tao, Zhong, Hao

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  12. 32
    FastLane: Test Minimization for Rapidly Deployed Large-Scale Online Services

    FastLane: Test Minimization for Rapidly Deployed Large-Scale Online Services by Philip, Adithya Abraham, Bhagwan, Ranjita, Kumar, Rahul, Maddila, Chandra Sekhar, Nagppan, Nachiappan

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  13. 33
    Do Crosscutting Concerns Cause Defects?

    Do Crosscutting Concerns Cause Defects? by Eaddy, M., Zimmermann, T., Sherwood, K.D., Garg, V., Murphy, G.C., Nagappan, N., Aho, A.V.

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  14. 34
    Automatic Static Vulnerability Detection for Machine Learning Libraries: Are We There Yet?

    Automatic Static Vulnerability Detection for Machine Learning Libraries: Are We There Yet? by Harzevili, Nima Shiri, Shin, Jiho, Wang, Junjie, Wang, Song, Nagappan, Nachiappan

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  15. 35
    Change Bursts as Defect Predictors

    Change Bursts as Defect Predictors by Nagappan, Nachiappan, Zeller, Andreas, Zimmermann, Thomas, Herzig, Kim, Murphy, Brendan

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  16. 36
    The design of bug fixes

    The design of bug fixes by Murphy-Hill, Emerson, Zimmermann, Thomas, Bird, Christian, Nagappan, Nachiappan

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  17. 37
    On the value of static analysis for fault detection in software

    On the value of static analysis for fault detection in software by Zheng, J., Williams, L., Nagappan, N., Snipes, W., Hudepohl, J.P., Vouk, M.A.

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  18. 38
    Characterizing and predicting which bugs get fixed: an empirical study of Microsoft Windows

    Characterizing and predicting which bugs get fixed: an empirical study of Microsoft Windows by Guo, Philip J., Zimmermann, Thomas, Nagappan, Nachiappan, Murphy, Brendan

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  19. 39
    Realizing quality improvement through test driven development: results and experiences of four industrial teams

    Realizing quality improvement through test driven development: results and experiences of four industrial teams by Nagappan, Nachiappan, Maximilien, E. Michael, Bhat, Thirumalesh, Williams, Laurie

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  20. 40
    Putting It All Together: Using Socio-technical Networks to Predict Failures

    Putting It All Together: Using Socio-technical Networks to Predict Failures by Bird, C., Nagappan, N., Gall, H., Murphy, B., Devanbu, P.

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