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Home > Groups > Software Analytics
Software Analytics

A huge wealth of various data exists in software lifecycle, including source code, feature specifications, bug reports, test cases, execution traces/logs, and real-world user feedback, etc. Data plays an essential role in modern software development, because hidden in the data is information about the quality of software and services as well as the dynamics of software development. With various analytical and computing technologies, such as pattern recognition, machine learning, data mining, information visualization and large-scale data computing & processing, software analytics is to enable software practitioners to perform effective and efficient data exploration and analysis in order to obtain insightful and actionable information for data-driven tasks in engineering software and services.

The mission of the Software Analytics Group at MSR Asia is to advance the state of the art in the software analytics area; and utilize our technologies to help improve the quality of software and services as well as the development productivity for both Microsoft and software industry.

  • Software Systems

Depending on scale and complexity, the spectrum of software systems can span from operating systems for devices to large networked systems that consist of thousands of servers. System quality such as reliability, performance and security, is the key to success of modern software systems. As the system scale and complexity greatly increase, larger amount of data, e.g., run-time traces and logs, is generated; and data has become a critical media to monitor, analyze, understand and improve system quality.

 

Our group has on-going efforts in this challenging yet promising area. For example, we are mining huge operating system traces to help domain experts quickly identify performance issues. We are researching analysis techniques and developing easy-to-use tools to assist performance diagnosis for large scale networked systems.

 

  • Software Users

Users are (almost) always right because ultimately they pay for the software and services in various ways. Therefore, it is important to continuously create the best user experience. Usage data collected from the real world reveals how users interact with software and services. The data is incredibly valuable for software practitioners to better understand their customers and gain insights on how to improve user experience accordingly.

 

Our group works on various usage data exploration and analysis projects. The goal is to provide advanced techniques to enable the acquisition of actionable information and driving informed decision for creating the best user experience.

 

  • Development Process

    • Software development has evolved from its traditional form to exhibit different characteristics. The process is more agile and engineers are more collaborative. Analytics on software development data provides a powerful mechanism that we can leverage in order to achieve higher development productivity.

     

    Our group is looking into interesting topics using the data-driven approach such as "how to find bad smells in source code", "how to support efficient code review and test selection", and "how to measure and predict the health of development process".  

 

Here at Microsoft, there are rich data and real problems providing us great opportunities to do excellent research in software analytics. We welcome new members to our group as well as collaborators from both internally and externally. If you are interested, please contact us at dongmeiz@microsoft.com.

 

Members
Yingnong Dang
Justin Ding
Qiang Fu
Song Ge
Song Ge

Shi Han
Zhitao Hou
Ray Huang
Ray Huang

Qingwei Lin
Jian-Guang Lou
Qiang Wang
Qiang Wang

Dongmei Zhang
Dongmei Zhang

Haidong Zhang
Haidong Zhang

Selected Publications

Shi Han, Yingnong Dang, Song Ge, Dongmei Zhang, and Tao Xie, Performance Debugging in the Large via Mining Millions of Stack Traces, to appear in Proceedings of the 34th International Conference on Software Engineering (ICSE 2012), Zurich, Switzerland, June 2012.

Yingnong Dang, Rongxin Wu, Hongyu Zhang, Dongmei Zhang and Peter Nobel, ReBucket – A Method for Clustering Duplicate Crash Reports based on Call Stack Similarity, to appear in Proceedings of the 34th International Conference on Software Engineering (ICSE 2012), Software Engineering in Practice, Zurich, Switzerland, June 2012.

Jian-Guang Lou, Qiang Fu, Shengqi Yang, Jiang Li, and Bin Wu, Mining Program Workflow from Interleaved Traces, in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'10), July 25, 2010.

Jian-Guang Lou, Qiang Fu, Shengqi Yang, Ye Xu, and Jiang Li, Mining Invariants from Console Logs for System Problem Detection, in Proceedings of the 2010 USENIX Annual Technical Conference, USENIX, June 2010.

Qiang Fu, Jian-Guang Lou, Yi Wang, and Jiang Li, Execution Anomaly Detection in Distributed Systems through Unstructured Log Analysis, in Proceedings of the 2009 IEEE International Conference on Data Mining, (ICDM’2009), December 9, 2009.