||1. 程尚文, 使用樣板教學於初階程式設計課程之探討. 2009, 國立中山大學; 資訊管理學系研究所: 高雄.|
2. Mow, I., Issues and Difficulties in Teaching Novice Computer Programming. Innovative Techniques in Instruction Technology, E-learning, E-assessment, and Education, 2008: p. 199-204.
3. Butler, M. and M. Morgan, Learning challenges faced by novice programming students studying high level and low feedback concepts. ASCILATE 2007 Singapore, 2007. 99: p. 107.
4. Lahtinen, E., K. Ala-Mutka, and H.M. Järvinen. A study of the difficulties of novice programmers. in Proceedings of the 10th annual SIGCSE conference on Innovation and technology in computer science education. 2005: ACM.
5. Ahmadzadeh, M., D. Elliman, and C. Higgins. An analysis of patterns of debugging among novice computer science students. in Proceedings of the 10th annual SIGCSE conference on Innovation and technology in computer science education. 2005. Monte de Cparica, Portugal.: ACM.
6. Fitzgerald, S., et al., Debugging: finding, fixing and flailing, a multi-institutional study of novice debuggers. Computer Science Education, 2008. 18(2): p. 93-116.
7. Murphy, L., et al. Debugging: the good, the bad, and the quirky--a qualitative analysis of novices' strategies. in Proceedings of the 39th SIGCSE technical symposium on Computer science education. 2008: ACM.
8. Rodrigo, M., et al. Affective and behavioral predictors of novice programmer achievement. in Proceedings of the 14th annual ACM SIGCSE conference on Innovation and technology in computer science education. 2009: ACM.
9. Lee, G.C. and J.C. Wu, Debug It: A debugging practicing system. Computers & Education, 1999. 32(2): p. 165-179.
10. Douce, C., D. Livingstone, and J. Orwell, Automatic test-based assessment of programming: A review. Journal on Educational Resources in Computing (JERIC), 2005. 5(3): p. 4.
11. Jackson, D. and M. Usher, Grading student programs using ASSYST. ACM SIGCSE Bulletin, 1997. 29(1): p. 335-339.
12. Leal, J.P. and F. Silva, Mooshak: a Web-based multi-site programming contest system. Software Focus, 2003. 33(6): p. 567-581.
13. Punitha, T., et al. Mooshak A Valuable Repository of Codes. 2008.
14. Thompson, S., An Exploratory Study of Novice Programming Experiences and Errors. 2004, Citeseer.
15. Ebrahimi, A., D. Kopec, and C. Schweikert, Taxonomy of Novice Programming Error Patterns with Plan, Web, and Object Solutions.
16. Hristova, M., et al., Identifying and correcting Java programming errors for introductory computer science students. ACM SIGCSE Bulletin, 2003. 35(1): p. 156.
17. Spohrer, J. and E. Soloway, Novice mistakes: Are the folk wisdoms correct? Communications of the ACM, 1986. 29(7): p. 624-632.
18. Someren, M., What's wrong? Understanding beginners' problems with Prolog. Instructional Science, 1990. 19(4): p. 257-282.
19. Soloway, E., Learning to program= learning to construct mechanisms and explanations. 1986.
20. Ko, A. and B. Myers, A framework and methodology for studying the causes of software errors in programming systems. Journal of Visual Languages & Computing, 2005. 16(1-2): p. 41-84.
21. McCauley, R., et al., Debugging: a review of the literature from an educational perspective. Computer Science Education, 2008. 18(2): p. 67-92.
22. Ducasse, M. and A.M. Emde. A review of automated debugging systems: knowledge, strategies and techniques. in Proceedings of the 10th international conference on Software engineering. 1998. Singapore: IEEE Computer Society Press Los Alamitos, CA, USA.
23. Ahoniemi, T., E. Lahtinen, and T. Reinikainen, Improving pedagogical feedback and objective grading. ACM SIGCSE Bulletin, 2008. 40(1): p. 72-76.
24. 林盟憲, 一個適用於個別練習之程式設計學習系統. 2008, 國立中山大學; 資訊管理學系研究所: 高雄.
25. Osterweil, L., Strategic directions in software quality. ACM Computing Surveys (CSUR), 1996. 28(4): p. 750.
26. 鄭炳強, 軟體工程：從實務出發. 2007, 台北市: 智勝文化.
27. Tripathy, K.N.P., Software Testing and Quality Assurance: Theory and Practice. 2008, Hoboken, New Jersey: John Wiley & Sons, Inc.
28. Jhala, R. and R. Majumdar, Software model checking. ACM Computing Surveys (CSUR), 2009. 41(4): p. 1-54.
29. Clarke, E., D. Kroening, and F. Lerda, A tool for checking ANSI-C programs. Lecture Notes in Computer Science, 2004: p. 168-176.
30. Sen, K. Concolic testing. 2007: ACM.
31. Kim, Y., M. Kim, and N. Dang, Scalable Distributed Concolic Testing: a Case Study on a Flash Storage Platform.
32. GNU Coreutils. Available from: http://www.gnu.org/software/coreutils/.
33. SGLIB. Available from: http://xref-tech.com/sglib/main.html.
34. Cadar, C., D. Dunbar, and D. Engler. Klee: Unassisted and automatic generation of high-coverage tests for complex systems programs. 2008.
35. Cadar, C. and D. Engler, Execution generated test cases: How to make systems code crash itself. Model Checking Software, 2005: p. 2-23.
36. Sen, K. and G. Agha. CUTE and jCUTE: Concolic unit testing and explicit path model-checking tools. 2006: Springer.
37. Sen, K., D. Marinov, and G. Agha, CUTE: A concolic unit testing engine for C. ACM SIGSOFT Software Engineering Notes, 2005. 30(5): p. 272.
38. Godefroid, P., N. Klarlund, and K. Sen. DART: Directed automated random testing. 2005: ACM.
39. Collofello Scott, N. and S. James, Evaluating the effectiveness of reliability-assurance techniques. Journal of Systems and Software, 1989. 9(3): p. 191-195.
40. Vessey, I., Expertise in debugging computer programs: A process analysis. International Journal of Man-Machine Studies, 1985. 23(5): p. 459-494.
41. Pan, H. and E. Spafford, Heuristics for automatic localization of software faults. World Wide Web, 1992.
42. Agrawal, H., et al., Fault localization using execution slices and dataflow tests. Proceedings of IEEE Software Reliability Engineering, 1995: p. 143¡V151.
43. Renieres, M. and S. Reiss. Fault localization with nearest neighbor queries. 2003.
44. Cleve, H. and A. Zeller. Locating causes of program failures. 2005.
45. Harrold, M., et al. An empirical investigation of program spectra. 1998: ACM.
46. Eric Wong, W., V. Debroy, and B. Choi, A family of code coverage-based heuristics for effective fault localization. Journal of Systems and Software, 2009.
47. Zeller, A., Isolating cause-effect chains from computer programs. ACM SIGSOFT Software Engineering Notes, 2002. 27(6): p. 10.
48. Jones, J. and M. Harrold. Empirical evaluation of the tarantula automatic fault-localization technique. 2005: ACM.
49. Vayani, R., Improving Automatic Software Fault Localization, in Computer Science. 2007, Delft University of Technology: Delft.
50. Jones, J., M. Harrold, and J. Stasko. Visualization of test information to assist fault localization. 2002: ACM.
51. Dallmeier, V., C. Lindig, and A. Zeller, Lightweight defect localization for Java. ECOOP 2005-Object-Oriented Programming, 2005: p. 528-550.
52. Wong, W., et al. Effective fault localization using code coverage. 2007.
53. Abreu, R., et al. Automatic software fault localization using generic program invariants. 2008: ACM.
54. Janssen, T., R. Abreu, and A. van Gemund. Zoltar: a spectrum-based fault localization tool. 2009: ACM.
55. Yu, Y., J. Jones, and M. Harrold. An empirical study of the effects of test-suite reduction on fault localization. 2008: ACM.
56. Liblit, B., et al. Scalable statistical bug isolation. 2005: ACM.
57. Abreu, R., P. Zoeteweij, and A. van Gemund. An evaluation of similarity coefficients for software fault localization. 2006.
58. Burnim, J. and K. Sen. Heuristics for scalable dynamic test generation. 2008: IEEE Computer Society.
59. Necula, G., et al. CIL: Intermediate language and tools for analysis and transformation of C programs. 2002: Springer.
60. Dutertre, B. and L. De Moura, The yices smt solver. 2006.
61. CREST. Available from: http://code.google.com/p/crest.
62. Abreu, R., P. Zoeteweij, and A. Van Gemund. On the accuracy of spectrum-based fault localization. 2007.
63. Jiang, B., et al. How well do test case prioritization techniques support statistical fault localization. 2009: IEEE.
64. Hao, D., et al., Test input reduction for result inspection to facilitate fault localization. Automated Software Engineering, 2010. 17(1): p. 5-31.
65. Wang, T. and A. Roychoudhury. Automated path generation for software fault localization. 2005: ACM.
66. Guo, L., A. Roychoudhury, and T. Wang. Accurately choosing execution runs for software fault localization. 2006: Springer.
67. Groce, A., et al., Error explanation with distance metrics. International Journal on Software Tools for Technology Transfer (STTT), 2006. 8(3): p. 229-247.
68. Ferrante, J., K. Ottenstein, and J. Warren, The program dependence graph and its use in optimization. ACM Transactions on Programming Languages and Systems (TOPLAS), 1987. 9(3): p. 349.
69. Coppit, D. and J. Lian. yagg: an easy-to-use generator for structured test inputs. 2005: ACM.
70. Daniel, B., et al. Automated testing of refactoring engines. 2007: ACM.
71. Godefroid, P., A. Kiezun, and M. Levin, Grammar-based whitebox fuzzing. ACM SIGPLAN Notices, 2008. 43(6): p. 206-215.
72. Dimitrov, M. and H. Zhou. Anomaly-based bug prediction, isolation, and validation: an automated approach for software debugging. 2009: ACM.