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Code CS 560
Term 201702
Title Automated Debugging
Faculty Faculty of Engineering and Natural Sciences
Subject Computer Sci.& Eng.(CS)
SU Credit 3
ECTS Credit 10.00
Instructor(s) Cemal Y?lmaz,
Language of Instruction English
Level of Course Doctoral
Type of Course Click here to view.
(only for SU students)
Mode of Delivery Formal lecture
Planned Learning Activities Discussion based learning,Project based learning,Other

Program debugging is a process of identifying and fixing bugs. Identifying root causes is the hardest, thus the most expensive, component of debugging. Developers often take a slice of the statements involved in a failure, hypothesize a set of potential causes in an ad hoc manner, and iteratively verify and refine their hypotheses until root causes are located. Obviously, this process can be quite tedious and time-consuming. Furthermore, as software systems are getting increasingly complex, the inefficiencies of the manual debugging process are getting magnified. Many automated approaches have been proposed to facilitate program debugging. All these approaches share the same ultimate goal, which is to help developers quickly and accurately pinpoint the root causes of failures. This course will cover state-of-the-art automated debugging approaches from both practical and research perspectives and will consist of two main parts. The goal of the first part is two folds: 1) To turn program debugging from a black art (as many believe) into a systematic and well-organized discipline; and 2) To provide students with enough background information to read and understand the scientific literature. The topics which will be covered in the first part are: How Failures Come To Be, Tracking Problems, Making Programs Fail, Reproducing Problems, Simplifying Problems, Scientific Debugging, Deducing Errors, and Mining and Detecting Anomalies. The second part of the course will survey the related literature by dividing it into four broad categories, namely static- analysis-based, dynamic-analysis-based, model-based, and empirical approaches.


To turn program debugging from a black magic (as many believe) into a systematic and well-organized discipline.
To introduce the principles of systematic program debugging.
To introduce the principles of automating the debugging process.
To introduce the basics of building automated debugging tools.
To introduce the basics of state-of-the-art debugging tools.
To survey the state-of-the-art approaches in the scientific literature.

Learning Outcome

Describe and apply the principles of systematic program debugging.
Identify, formulate, analyze, and solve basic problems related to automating the debugging process.
Design and implement basic automated debugging tools.
Use, analyze, and evaluate state-of-the-art debugging tools.
Read and understand the related scientific literature.

Programme Outcomes
Common Outcomes For All Programs
1 Develop and deepen the current and advanced knowledge in the field with original thought and/or research and come up with innovative definitions based on Master's degree qualifications 4
2 Conceive the interdisciplinary interaction which the field is related with ; come up with original solutions by using knowledge requiring proficiency on analysis, synthesis and assessment of new and complex ideas. 4
3 Evaluate and use new information within the field in a systematic approach. 5
4 Develop an innovative knowledge, method, design and/or practice or adapt an already known knowledge, method, design and/or practice to another field; research, conceive, design, adapt and implement an original subject. 5
5 Critical analysis, synthesis and evaluation of new and complex ideas. 5
6 Gain advanced level skills in the use of research methods in the field of study. 5
7 Contribute the progression in the field by producing an innovative idea, skill, design and/or practice or by adapting an already known idea, skill, design, and/or practice to a different field independently. 4
8 Broaden the borders of the knowledge in the field by producing or interpreting an original work or publishing at least one scientific paper in the field in national and/or international refereed journals. 2
9 Demonstrate leadership in contexts requiring innovative and interdisciplinary problem solving. 1
10 Develop new ideas and methods in the field by using high level mental processes such as creative and critical thinking, problem solving and decision making. 4
11 Investigate and improve social connections and their conducting norms and manage the actions to change them when necessary. 1
12 Defend original views when exchanging ideas in the field with professionals and communicate effectively by showing competence in the field. 4
13 Ability to communicate and discuss orally, in written and visually with peers by using a foreign language at least at a level of European Language Portfolio C1 General Level. 3
14 Contribute to the transition of the community to an information society and its sustainability process by introducing scientific, technological, social or cultural improvements. 1
15 Demonstrate functional interaction by using strategic decision making processes in solving problems encountered in the field. 1
16 Contribute to the solution finding process regarding social, scientific, cultural and ethical problems in the field and support the development of these values. 2
Common Outcomes For All Programs
1 Develop the ability to use critical, analytical, and reflective thinking and reasoning 5
2 Reflect on social and ethical responsibilities in his/her professional life. 1
3 Gain experience and confidence in the dissemination of project/research outputs 4
4 Work responsibly and creatively as an individual or as a member or leader of a team and in multidisciplinary environments. 2
5 Communicate effectively by oral, written, graphical and technological means and have competency in English. 2
6 Independently reach and acquire information, and develop appreciation of the need for continuously learning and updating. 4
Common Outcomes ForFaculty of Eng. & Natural Sci.
1 Design and model engineering systems and processes and solve engineering problems with an innovative approach. 5
2 Establish experimental setups, conduct experiments and/or simulations. 5
3 Analytically acquire and interpret data. 1
Electronics Engineering (with thesis) Program Outcomes Core Electives
1 Use advanced Math (including probability and/or statistics), advanced sciences, advanced computer and programming, and advanced Electronics engineering knowledge to design and analyze complex electronics circuits, instruments, software and electronic systems with hardware/software. 2
2 Analyze and design advanced communication networks and systems, advanced signal processing algorithms or software using advanced knowledge on diff. equations, linear algebra, complex variables and discrete math. 1
Physics (non-thesis) Program Outcomes Core Electives
1 Employ mathematical methods to solve physical problems and understand relevant numerical techniques. 1
2 Conduct basic experiments or simulations. 2
3 Analytically acquire and interpret data. 1
4 Establish thorough understanding of the fundamental principles of physics. 1
Computer Science and Engineering (with thesis) Program Outcomes Core Electives
1 Apply knowledge of mathematics, science, and engineering in computer science and engineering related problems. 5
2 Display knowledge of contemporary issues in computer science and engineering and apply to a particular problem. 5
3 Demonstrate the use of results from interpreted data to improve the quality of research or a product in computer science and engineering. 5
Energy Technologies and Management (with thesis) Program Outcomes Area Electives
1 Assess and identify developments, strategies, opportunities and problems in energy security and energy technologies. 1
2 Define and solve technical, economic and administrative problems in energy businesses. 1
3 Establish knowledge and understanding of energy security, energy technologies, energy markets and strategic planning in energy enterprises. 1
4 Demonstrate an awareness of environmental concerns and their importance in developing engineering solutions and new technologies. 1
5 Acquire a series of social and technical proficiencies for project management and leadership skills. 1
Materials Science and Engineering (with thesis) Program Outcomes Area Electives
1 Apply a broad knowledge of structure & microstructure of all classes of materials, and the ability to use this knowledge to determine the material properties. 1
2 Apply a broad understanding of the relationships between material properties, performance and processing. 1
3 Apply a broad understanding of thermodynamics, kinetics, transport phenomena, phase transformations and materials aspects of advanced technology. 1
4 Demonstrate hands-on experience using a wide range of materials characterization techniques. 1
5 Demonstrate the use of results from interpreted data to improve the quality of research, a product, or a product in materials science and engineering. 1
Mechatronics Engineering (with thesis) Program Outcomes Area Electives
1 Apply software, modeling, instrumentation, and experimental techniques and their combinations in the design and integration of electrical, electronic, control and mechanical systems. 2
2 Interact with researchers from different disciplines to exchange ideas and identify areas of research collaboration to advance the frontiers of present knowledge and technology; determine relevant solution approaches and apply them by preparing a research strategy. 1
3 Take part in ambitious and highly challenging research to generate value for both the industry and society. 1
Molecular Biology, Genetics and Bioengineering (with thesis) Program Outcomes Area Electives
1 Apply knowledge of key concepts in biology, with an emphasis on molecular genetics, biochemistry and molecular and cell biology. 1
2 Display an awareness of the contemporary biological issues in relation with other scientific areas. 1
3 Demonstrate hands-on experience in a wide range of biological experimental techniques. 1
Industrial Engineering (with thesis) Program Outcomes Area Electives
1 Establish a strong theoretical background in several of a broad range of subjects related to the discipline, such as manufacturing processes, service systems design and operation, production planning and control, modeling and optimization, stochastics, statistics. 1
2 Develop novel modeling and / or analytical solution strategies for problems in integrated production and service systems involving human capital, materials, information, equipment, and energy, also using an interdisciplinary approach whenever appropriate. 1
3 Implement solution strategies on a computer platform for decision-support purposes by employing effective computational and experimental tools. 2
4 Acquire skills to independently explore and tackle problems related to the discipline that were not encountered previously. Develop appropriate modeling, solution, implementation strategies, and assess the quality of the outcome. 1
Assessment Methods and Criteria
  Percentage (%)
Final 25
Participation 5
Group Project 50
Presentation 20
Recommended or Required Reading

Andreas Zeller, "Why Programs Fail: A Guide to Systematic Debugging?", Morgan Kaufmann, ISBN: 1558608664 (Recommended)


Static-Analysis-Based Approaches

D. Engler, B. Chelf, A. Chou et al., ?Checking system rules using system specific, programmer-written compiler extensions,? in Proceedings of the 4th conference on Symposium on Operating System Design & Implementation, Volume 4, San Diego, California, 2000.

D. Hovemeyer, and W. Pugh, ?Finding bugs is easy,? SIGPLAN Not., vol. 39, no. 12, pp. 92-106, 2004.

Y. Xie, and D. Engler, ?Using redundancies to find errors,? IEEE Transactions on Software Engineering, vol. 29, no. 10, pp. 915-928, 2003.

Dynamic-Analysis-Based Approaches

B. Liblit, A. Aiken, A. X. Zheng et al., ?Bug isolation via remote program sampling,? SIGPLAN Not., vol. 38, no. 5, pp. 141-154, 2003.

J. A. Jones, and M. J. Harrold, ?Empirical evaluation of the tarantula automatic fault-localization technique,? in Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering, Long Beach, CA, USA, 2005, pp. 273-282.

H. Agrawal, J. R. Horgan, S. London et al., ?Fault Localization using Execution Slices and Dataflow Tests,? in Proceedings of the 6th International Symposium on Software Reliability Engineering, Toulouse, France, 1995, pp. 143-151.

M. Renieris, and S. P. Reiss, ?Fault Localization With Nearest Neighbor Queries,? in Proceedings of the 18th IEEE International Conference on Automated Software Engineering, Montreal, Canada, 2003, pp. 30-39.

M. Stoerzer, B. G. Ryder, X. Ren et al., ?Finding failure-inducing changes in java programs using change classification,? in Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering, Portland, Oregon, USA, 2006, pp. 57-68.

V. Dallmeier, C. Lindig, and A. Zeller, "Lightweight defect localization for java."

D. Abramson, I. Foster, J. Michalakes et al., ?Relative debugging and its application to the development of large numerical models,? in Proceedings of the 1995 ACM/IEEE conference on Supercomputing (CDROM), San Diego, California, United States, 1995.

B. Liblit, M. Naik, A. X. Zheng et al., ?Scalable statistical bug isolation,? in Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation, Chicago, IL, USA, 2005, pp. 15-26.

S. Hangal, and M. S. Lam, ?Tracking down software bugs using automatic anomaly detection,? in Proceedings of the 24th International Conference on Software Engineering, Orlando, Florida, 2002, pp. 291-301.

J. A. Jones, M. J. Harrold, and J. Stasko, ?Visualization of test information to assist fault localization,? in Proceedings of the 24th International Conference on Software Engineering, Orlando, Florida, 2002, pp. 467-477.

C. Yilmaz, A. Paradkar, and C. Williams, ?Time will tell: fault localization using time spectra,? in Proceedings of the 30th international Conference on Software Engineering, Leipzig, Germany, 2008, pp. 81-90.

Model-Based Approaches

C. Yilmaz, and C. Williams, ?An automated model-based debugging approach,? in Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering, Atlanta, Georgia, USA, 2007, pp. 174-183.

C. Mateis, M. Stumptner, D. Wieland et al., "Extended Abstract ? Model-Based Debugging of Java Programs," 2000.

C. Mateis, M. Stumptner, D. Wieland et al., ?Model-Based Debugging of Java Programs,? in Proceedings of the International Workshop on Automated Debugging, 2000.

Empirical Approaches

A. Zeller, ?Isolating cause-effect chains from computer programs,? in Proceedings of the 10th ACM SIGSOFT symposium on Foundations of software engineering, Charleston, South Carolina, USA, 2002, pp. 1-10.

A. Zeller, and R. Hildebrandt, ?Simplifying and Isolating Failure-Inducing Input,? IEEE Trans. Softw. Eng., vol. 28, no. 2, pp. 183-200, 2002.

C. Yilmaz, M. Cohen, and A. Porter, ?Covering arrays for efficient fault characterization in complex configuration spaces,? IEEE Trans. Softw. Eng., vol. 32, no. 1, pp. 20-34, 2006.

Other Related Papers

A. Zeller, ?Automated Debugging: Are We Close,? Computer, vol. 34, no. 11, pp. 26-31, 2001.

M. D. Ernst, J. Cockrell, W. G. Griswold et al., ?Dynamically discovering likely program invariants to support program evolution,? in Proceedings of the 21st international conference on Software engineering, Los Angeles, California, United States, 1999, pp. 213-224.

R. Reiter, ?A theory of diagnosis from first principles,? Artificial Intelligence, vol. 32, no. 1, pp. 57-95, 1987.

T. Reps, T. Ball, M. Das et al., ?The use of program profiling for software maintenance with applications to the year 2000 problem,? in Proceedings of the 6th European conference held jointly with the 5th ACM SIGSOFT international symposium on Foundations of software engineering, Zurich, Switzerland, 1997, pp. 432-449.