Skip to main content
SpringerLink
Log in

High-confidence software evolution

  • Review
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

Software continues to evolve due to changing requirements, platforms and other environmental pressures. Modern software is dependent on frameworks, and if the frameworks evolve, the software has to evolve as well. On the other hand, the software may be changed due to changing requirements. Therefore, in high-confidence software evolution, we must consider both framework evolution and client evolution, each of which may incur faults and reduce software quality. In this article, we present a set of approaches to address some problems in high-confidence software evolution. In particular, to support framework evolution, we propose a history-based matching approach to identify a set of transformation rules between different APIs, and a transformation language to support automatic transformation. To support client evolution for high-confidence software, we propose a path-exploration-based approach to generate tests efficiently by pruning paths irrelevant to changes between versions, several coverage-based approaches to optimize test execution, and approaches to locate faults and fix memory leaks automatically. These approaches facilitate high-confidence software evolution from various aspects.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Godfrey M W, German D M. The past, present, and future of software evolution. In: Proceedings of Frontiers of Software Maintenance, Beijing, 2008. 129–138

    Google Scholar 

  2. Kemerer C, Slaughter S. An empirical approach to studying software evolution. IEEE Trans Softw Eng, 1999, 25: 493–509

    Article  Google Scholar 

  3. Lehman M M, Ramil J F, Wernick P D, et al. Metrics and laws of software evolution - the nineties view. In: Proceedings of the 4th International Software Metrics Symposium, Albuquerque, 1997. 20–32

    Chapter  Google Scholar 

  4. Böhme M, Oliveira B C d S, Roychoudhury A. Regression tests to expose change interaction errors. In: Proceedings of the 9th Joint Meeting on Foundations of Software Engineering. New York: ACM, 2013. 334–344

    Google Scholar 

  5. Dig D, Manzoor K, Johnson R, et al. Refactoring-aware configuration management for object-oriented programs. In: Proceedings of the 29th International Conference on Software Engineering, Minneapolis, 2007. 427–436

    Google Scholar 

  6. Harrold M J, Gupta R, Soffa M L. A methodology for controlling the size of a test suite. ACM Trans Softw Eng Methodol, 1993, 2: 270–285

    Article  Google Scholar 

  7. Henkel J, Diwan A. Catchup! capturing and replaying refactorings to support API evolution. In: Proceedings of the 27th International Conference on Software Engineering. New York: ACM, 2005. 274–283

    Google Scholar 

  8. Ren X, Ryder B G. Heuristic ranking of Java program edits for fault localization. In: Proceedings of the International Symposium on Software Testing and Analysis. New York: ACM, 2007. 239–249

    Google Scholar 

  9. Santelices R, Harrold M J, Orso A. Precisely detecting runtime change interactions for evolving software. In: Proceedings of the 3rd International Conference on Software Testing, Verification and Validation, Paris, 2010. 429–438

    Google Scholar 

  10. Stoerzer M, Ryder B G, Ren X, 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. New York: ACM, 2006. 57–68

    Chapter  Google Scholar 

  11. Wu W, Guéhéneuc Y-G, Antoniol G, et al. Aura: a hybrid approach to identify framework evolution. In: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering. New York: ACM, 2010. 325–334

    Google Scholar 

  12. Yoo S, Harman M. Using hybrid algorithm for pareto efficient multi-objective test suite minimisation. J Syst Softw, 2010, 83: 689–701

    Article  Google Scholar 

  13. Zhang W, Yi L, Zhao H Y, et al. Feature-oriented stigmergy-based collaborative requirements modeling: an exploratory approach for requirements elicitation and evolution based on web-enabled collective intelligence. Sci China Inf Sci, 2013, 56: 082108

    Google Scholar 

  14. Kim M, Notkin D, Grossman D. Automatic inference of structural changes for matching across program versions. In: Proceedings of the International Conference on Software Engineering, Minneapolis, 2007. 333–343

    Google Scholar 

  15. Xing Z, Stroulia E. API-evolution support with Diff-CatchUp. IEEE Trans Softw Eng, 2007, 33: 818–836

    Article  Google Scholar 

  16. Malpohl G, Hunt J, Tichy W. Renaming detection. In: Proceedings of the 15th IEEE International Conference on Automated Software Engineering, Grenoble, 2000. 73–80

    Google Scholar 

  17. Weissgerber P, Diehl S. Identifying refactorings from source-code changes. In: Proceedings of the 21st IEEE/ACM International Conference on Automated Software Engineering (ASE’06), Tokyo, 2006. 231–240

    Chapter  Google Scholar 

  18. Godfrey M, Zou L. Using origin analysis to detect merging and splitting of source code entities. IEEE Trans Softw Eng, 2005, 31: 166–181

    Article  Google Scholar 

  19. Nguyen H A, Nguyen T T, Wilson G, et al. A graph-based approach to API usage adaptation. In: Proceedings of the ACM International Conference on Object Oriented Programming Systems Languages and Applications. New York: ACM, 2010. 302–321

    Chapter  Google Scholar 

  20. Meng S, Wang X, Zhang L, et al. A history-based matching approach to identification of framework evolution. In: Proceedings of the 34th International Conference on Software Engineering (ICSE), Zurich, 2012. 353–363

    Google Scholar 

  21. Li J, Wang C, Xiong Y, et al. SWIN: towards type-safe Java program adaptation between APIs. In: Proceedings of the Workshop on Partial Evaluation and Program Manipulation. New York: ACM, 2015. 91–102

    Google Scholar 

  22. Wang C L, Li J, Xiong Y F, et al. Formal Definition of SWIN Language. https://github.com/Mestway/SWINProject/ blob/master/docs/pepm-15/TR/TR.pdf. 2014

    Google Scholar 

  23. Nita M, Notkin D. Using twinning to adapt programs to alternative APIs. In: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering, New York: ACM, 2010. 205–214

    Google Scholar 

  24. Cordy J R. The TXL source transformation language. Sci Comput Program, 2006, 61: 190–210

    Article  MathSciNet  MATH  Google Scholar 

  25. Bravenboer M, Kalleberg K T, ermaas R V, et al. Stratego/XT 0.17. a language and toolset for program transformation. Sci Comput Program, 2008, 72: 52–70

    Article  MathSciNet  Google Scholar 

  26. Wasserman L. Scalable, example-based refactorings with refaster. In: Proceedings of the ACM Workshop on Workshop on Refactoring Tools. New York: ACM, 2013. 25–28

    Chapter  Google Scholar 

  27. Erwig M, Ren D. A rule-based language for programming software updates. In: Proceedings of the ACM SIGPLAN Workshop on Rule-Based Programming. New York: ACM, 2002. 67–78

    Google Scholar 

  28. Erwig M, Ren D. An update calculus for expressing type-safe program updates. Sci Comput Program, 2007, 67: 199–222

    Article  MathSciNet  MATH  Google Scholar 

  29. Dig D, Negara S, Mohindra V, et al. Reba: refactoring-aware binary adaptation of evolving libraries. In: Proceedings of the 30th International Conference on Software Engineering. New York: ACM, 2008. 441–450

    Google Scholar 

  30. Leather S, Jeuring J, Löh A, et al. Type-changing rewriting and semantics-preserving transformation. In: Proceedings of the ACM SIGPLAN Workshop on Partial Evaluation and Program Manipulation. New York: ACM, 2014. 109–120

    Google Scholar 

  31. Saff D, Ernst M D. Reducing wasted development time via continuous testing. In: Proceedings of the 14th International Symposium on Software Reliability Engineering, Denver, 2003. 281–292

    Google Scholar 

  32. Voas J. PIE: a dynamic failure-based technique. IEEE Trans Softw Eng, 1992, 18: 717–727

    Article  Google Scholar 

  33. Clarke L. A system to generate test data and symbolically execute programs. IEEE Trans Softw Eng, 1976, 2: 215–222

    Article  MathSciNet  Google Scholar 

  34. Csallner C, Smaragdakis Y. JCrasher: an automatic robustness tester for Java. Softw Pract Exp, 2004, 34: 1025–1050

    Article  Google Scholar 

  35. Godefroid P, Klarlund N, Sen K. DART: directed automated random testing. In: Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation. New York: ACM, 2005. 213–223

    Google Scholar 

  36. Pacheco C, Lahiri S K, Ernst M D, et al. Feedback-directed random test generation. In: Proceedings of the 29th International Conference on Software Engineering (ICSE’07), Minneapolis, 2007. 75–84

    Chapter  Google Scholar 

  37. Sen K, Marinov D, Agha G. CUTE: a concolic unit testing engine for C. In: Proceedings of the 10th European Software Engineering Conference Held Jointly with the 13th ACM SIGSOFT International Symposium on Foundations of Software Engineering. New York: ACM, 2005. 263–272

    Google Scholar 

  38. Tian T, Gong D-W. Test data generation for path coverage of message-passing parallel programs based on coevolutionary genetic algorithms. Aut Softw Eng, 2014, 22: 1–32

    Google Scholar 

  39. Tillmann N, Halleux J de. Pex-white box test generation for.NET. In: Tests and Proofs. Berlin: Springer, 2008. 134–153

    Chapter  Google Scholar 

  40. Tonella P. Evolutionary testing of classes. In: Proceedings of the ACM SIGSOFT International Symposium on Software Testing and Analysis. New York: ACM, 2004. 119–128

    Google Scholar 

  41. Visser W, Pasareanu C S, Khurshid S. Test input generation with Java pathfinder. In: Proceedings of the ACM SIGSOFT International Symposium on Software Testing and Analysis. New York: ACM, 2004. 97–107

    Google Scholar 

  42. Zhang W-Q, Gong D-W, Yao X-J, et al. Evolutionary generation of test data for many paths coverage. In: Proceedings of Chinese Control and Decision Conference, Xuzhou, 2010. 230–235

    Google Scholar 

  43. Inkumsah K, Xie T. Improving structural testing of object-oriented programs via integrating evolutionary testing and symbolic execution. In: Proceedings of the 23rd IEEE/ACM International Conference on Automated Software Engineering, L’Aquila, 2008. 297–306

    Google Scholar 

  44. Taneja K, Xie T, Tillmann N, et al. eXpress: guided path exploration for efficient regression test generation. In: Proceedings of the International Symposium on Software Testing and Analysis. New York: ACM, 2011. 1–11

    Google Scholar 

  45. Taneja K, Xie T, Tillmann N, et al. Guided path exploration for regression test generation. In: Proceedings of the 31st International Conference on Software Engineering, Vancouver, 2009. 311–314

    Google Scholar 

  46. Marinescu P D, Cadar C. Make test-zesti: a symbolic execution solution for improving regression testing. In: Proceedings of the 34th International Conference on Software Engineering. Piscataway: IEEE Press, 2012. 716–726

    Google Scholar 

  47. Böhme M, Oliveira B C d S, Roychoudhury A. Partition-based regression verification. In: Proceedings of the International Conference on Software Engineering, Piscataway, 2013. 302–311

    Google Scholar 

  48. Chipounov V, Georgescu V, Zamfir C, et al. Selective symbolic execution. In: Proceedings of the 5th Workshop on Hot Topics in System Dependability, Lisbon, 2009. 1–6

    Google Scholar 

  49. Chipounov V, Kuznetsov V, Candea G. The S2E platform: design, implementation, and applications. ACM Trans Comput Syst, 2012, 30: 1–49

    Article  Google Scholar 

  50. Fraser G, Arcuri A. Sound empirical evidence in software testing. In: Proceedings of the 34th International Conference on Software Engineering (ICSE), Zurich, 2012. 178–188

    Google Scholar 

  51. Jaygarl H, Kim S, Xie T, et al. OCAT: object capture-based automated testing. In: Proceedings of the 19th International Symposium on Software Testing and Analysis. New York: ACM, 2010. 159–170

    Google Scholar 

  52. Xiao X, Li S, Xie T, et al. Characteristic studies of loop problems for structural test generation via symbolic execution. In: Proceedings of IEEE/ACM 28th International Conference on Automated Software Engineering (ASE), Silicon Valley, 2013. 246–256

    Google Scholar 

  53. Xiao X, Xie T, Tillmann N, et al. Precise identification of problems for structural test generation. In: Proceedings of the 33rd International Conference on Software Engineering. New York: ACM, 2011. 611–620

    Google Scholar 

  54. Thummalapenta S, Xie T, Tillmann N, et al. Synthesizing method sequences for high-coverage testing. In: Proceedings of the ACM International Conference on Object Oriented Programming Systems Languages and Applications. New York: ACM, 2011. 189–206

    Google Scholar 

  55. Thummalapenta S, Xie T, Tillmann N, et al. MSeqGen: object-oriented unit-test generation via mining source code. In: Proceedings of the 7th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering. New York: ACM, 2009. 193–202

    Google Scholar 

  56. Qi D, Sumner W N, Qin F, et al. Modeling software execution environment. In: Proceedings of the 19th Working Conference on Reverse Engineering, Kingston, 2012. 415–424

    Google Scholar 

  57. Samimi H, Hicks R, Fogel A, et al. Declarative mocking. In: Proceedings of the International Symposium on Software Testing and Analysis. New York: ACM, 2013. 246–256

    Google Scholar 

  58. Zhang L, Ma X, Lu J, et al. Environmental modeling for automated cloud application testing. IEEE Softw, 2012, 29: 30–35

    Article  Google Scholar 

  59. Godefroid P, Luchaup D. Automatic partial loop summarization in dynamic test generation. In: Proceedings of the International Symposium on Software Testing and Analysis. New York: ACM, 2011. 23–33

    Google Scholar 

  60. Xie T, Tillmann N, de Halleux P, et al. Fitness-guided path exploration in dynamic symbolic execution. In: Proceedings of IEEE/IFIP International Conference on Dependable Systems & Networks, Lisbon, 2009. 359–368

    Google Scholar 

  61. Böhme M. Automated regression testing and verification of complex code changes. Dissertation for Ph.D. Degree. Singapore: National University of Singapore, 2014

    Google Scholar 

  62. Jones J A, Harrold M J. Test-suite reduction and prioritization for modified condition/decision coverage. IEEE Trans Softw Eng, 2003, 29: 195–209

    Article  Google Scholar 

  63. Jeffrey D, Gupta N. Improving fault detection capability by selectively retaining test cases during test suite reduction. IEEE Trans Softw Eng, 2007, 33: 108–123

    Article  Google Scholar 

  64. Lin J-W, Huang C-Y. Analysis of test suite reduction with enhanced tie-breaking techniques. Inf Softw Tech, 2009, 51: 679–690

    Article  Google Scholar 

  65. Chen T Y, Lau M F. A new heuristic for test suite reduction. Inf Softw Tech, 1998, 40: 347–354

    Article  Google Scholar 

  66. Hao D, Zhang L, Wu X, et al. On-demand test suite reduction. In: Proceedings of the 34th International Conference on Software Engineering, Piscataway, 2012. 738–748

    Google Scholar 

  67. Do H, Rothermel G. A controlled experiment assessing test case prioritization techniques via mutation faults. In: Proceedings of the 21st IEEE International Conference on Software Maintenance, Budapest, 2005. 411–420

    Google Scholar 

  68. Elbaum S, Malishevsky A, Rothermel G. Prioritizing test cases for regression testing. In: Proceedings of International Symposium of Software Testing and Analysis, Portland, 2000. 102–112

    Chapter  Google Scholar 

  69. Elbaum S, Malishevsky A, Rothermel G. Incorporating varying test costs and fault severities into test case prioritization. In: Proceedings of the 23rd International Conference on Software Engineering, Washington, 2001. 329–338

    Google Scholar 

  70. Hou S S, Zhang L, Xie T, et al. Quota-constrained test-case prioritization for regression testing of service-centric systems. In: Proceedings of IEEE International Conference on Software Maintenance, Beijing, 2008. 257–266

    Google Scholar 

  71. Jiang B, Zhang Z, Chan W K, et al. Adaptive random test case prioritization. In: Proceedings of the 24th IEEE/ACM International Conference on Automated Software Engineering, Auckland, 2009. 257–266

    Google Scholar 

  72. Li Z, Harman M, Hierons R. Search algorithms for regression test case prioritisation. IEEE Trans Softw Eng, 2007, 33: 225–237

    Article  Google Scholar 

  73. Zhang L, Hou S, Guo C, et al. Time-aware test-case prioritization using integer linear programming. In: Proceedings of the 18th International Symposium on Software Testing and Analysis. New York: ACM, 2009. 213–224

    Google Scholar 

  74. Hao D, Zhang L, Zhang L, et al. A unified test case prioritization approach. ACM Trans Softw Eng Methodol, 2014, 24: 1–31

    Article  Google Scholar 

  75. Zhang L, Hao D, Zhang L, et al. Bridging the gap between the total and additional test-case prioritization strategies. In: Proceedings of the 35th International Conference on Software Engineering (ICSE), San Francisco, 2013. 192–201

    Google Scholar 

  76. Hao D, Zhao X, Zhang L. Adaptive test-case prioritization guided by output inspection. In: Proceedings of the 37th Annual Computer Software and Applications Conference (COMPSAC), Kyoto, 2013. 169–179

    Google Scholar 

  77. Mei H, Hao D, Zhang L, et al. A static approach to prioritizing JUnit test cases. IEEE Trans Softw Eng, 2012, 38: 1258–1275

    Article  Google Scholar 

  78. Jones J A, Harrold M J, Stasko J. Visualization of test information to assist fault localization. In: Proceedings of the 24th International Conference on Software Engineering. New York: ACM, 2002. 467–477

    Google Scholar 

  79. Hao D, Zhang L, Pan Y, et al. On similarity-awareness in testing-based fault localization. Aut Softw Eng, 2008, 15: 207–249

    Article  Google Scholar 

  80. Liblit B, Naik M, Zheng A X, et al. Scalable statistical bug isolation. In: Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation. New York: ACM, 2005. 15–26

    Google Scholar 

  81. Yu Y, Jones J A, Harrold M J. An empirical study of the effects of test-suite reduction on fault localization. In: Proceedings of the 30th International Conference on Software Engineering. New York: ACM, 2008. 201–210

    Google Scholar 

  82. Abreu R, Zoeteweij P, van Gemund A J C. On the accuracy of spectrum-based fault localization. In: Proceedings of Testing: Academic and Industrial Conference Practice and Research Techniques - MUTATION, Windsor, 2007. 89–98

    Google Scholar 

  83. Papadakis M, Traon Y L. Using mutants to locate “unknown” faults. In: Proceedings of IEEE 5th International Conference on Software Testing, Verification and Validation, Montreal, 2012. 691–700

    Google Scholar 

  84. Zhang X, Gupta N, Gupta R. Locating faults through automated predicate switching. In: Proceedings of the 28th International Conference on Software Engineering. New York: ACM, 2006. 272–281

    Google Scholar 

  85. Jeffrey D, Gupta N, Gupta R. Fault localization using value replacement. In: Proceedings of the International Symposium on Software Testing and Analysis. New York: ACM, 2008. 167–178

    Google Scholar 

  86. Zhang S, Zhang C, Ernst M D. Automated documentation inference to explain failed tests. In: Proceedings of the 26th IEEE/ACM International Conference on Automated Software Engineering (ASE), Lawrence, 2011. 63–72

    Google Scholar 

  87. Xuan J, Monperrus M. Test case purification for improving fault localization. In: Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering. New York: ACM, 2014. 52–63

    Google Scholar 

  88. Zeller A. Yesterday, my program worked. today, it does not. why? In: Software Engineering — ESEC/FSE’99. Berlin: Springer, 1999. 253–267

    Google Scholar 

  89. Zeller A, Hildebrandt R. Simplifying and isolating failure-inducing input. IEEE Trans Softw Eng, 2002, 28: 183–200

    Article  Google Scholar 

  90. Zhang L, Kim M, Khurshid S. Localizing failure-inducing program edits based on spectrum information. In: Proceedings of the 27th IEEE International Conference on Software Maintenance (ICSM), Williamsburg, 2011. 23–32

    Google Scholar 

  91. Alves E, Gligoric M, Jagannath V, et al. Fault-localization using dynamic slicing and change impact analysis. In: Proceedings of the 26th IEEE/ACM International Conference on Automated Software Engineering, Washington, 2011. 520–523

    Google Scholar 

  92. Zhang L, Zhang L, Khurshid S. Injecting mechanical faults to localize developer faults for evolving software. In: Proceedings of the ACM SIGPLAN International Conference on Object Oriented Programming Systems Languages & Applications. New York: ACM, 2013. 765–784

    Google Scholar 

  93. Kim D, Nam J, Song J, et al. Automatic patch generation learned from human-written patches. In: Proceedings of the International Conference on Software Engineering. Piscataway: IEEE Press, 2013. 802–811

    Google Scholar 

  94. Goues C L, Nguyen T, Forrest S, et al. Genprog: a generic method for automatic software repair. IEEE Trans Softw Eng, 2012, 38: 54–72

    Article  Google Scholar 

  95. Nguyen H D T, Qi D, Roychoudhury A, et al. Semfix: program repair via semantic analysis. In: Proceedings of the International Conference on Software Engineering. Piscataway: IEEE Press, 2013. 772–781

    Google Scholar 

  96. Qi Y, Mao X, Lei Y, et al. The strength of random search on automated program repair. In: Proceedings of the 36th International Conference on Software Engineering. New York: ACM, 2014. 254–265

    Google Scholar 

  97. Nguyen T T, Nguyen H A, Pham N H, et al. Recurring bug fixes in object-oriented programs. In: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering. New York: ACM, 2010. 315–324

    Google Scholar 

  98. Coker Z, Hafiz M. Program transformations to fix C integers. In: Proceedings of the International Conference on Software Engineering. Piscataway: IEEE Press, 2013. 792–801

    Google Scholar 

  99. Jin G, Song L, Zhang W, et al. Automated atomicity-violation fixing. In: Proceedings of the 32nd ACM SIGPLAN Conference on Programming Language Design and Implementation. New York: ACM, 2011. 389–400

    Chapter  Google Scholar 

  100. Jin G, Zhang W, Deng D, et al. Automated concurrency-bug fixing. In: Proceedings of the 10th USENIX Symposium on Operating Systems Design and Implementation, Hollywood, 2012. 221–236

    Google Scholar 

  101. Li H H, Qi J, Liu F, et al. The research progress of fuzz testing technology (in Chinese). Sci China Inform, 2014, 44: 1305–1322

    MathSciNet  Google Scholar 

  102. Xie T, Zhang L, Xiao X, et al. Cooperative software testing and analysis: advances and challenges. J Comput Sci Tech, 2014, 29: 713–723

    Article  Google Scholar 

  103. Cherem S, Princehouse L, Rugina R. Practical memory leak detection using guarded value-flow analysis. In: Proceedings of the 28th ACM SIGPLAN Conference on Programming Language Design and Implementation. New York: ACM, 2007. 480–491

    Google Scholar 

  104. Hackett B, Rugina R. Region-based shape analysis with tracked locations. In: Proceedings of the 32nd ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages. New York: ACM, 2005. 310–323

    Google Scholar 

  105. Heine D L, Lam M S. A practical flow-sensitive and context-sensitive C and C++ memory leak detector. In: Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation. New York: ACM, 2003. 168–181

    Google Scholar 

  106. Jung Y, Yi K. Practical memory leak detector based on parameterized procedural summaries. In: Proceedings of the 7th International Symposium on Memory Management. New York: ACM, 2008. 131–140

    Google Scholar 

  107. Sui Y, Ye D, Xue J. Static memory leak detection using full-sparse value-flow analysis. In: Proceedings of the International Symposium on Software Testing and Analysis. New York: ACM, 2012. 254–264

    Google Scholar 

  108. Torlak E, Chandra S. Effective interprocedural resource leak detection. In: Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering. New York: ACM, 2010. 535–544

    Google Scholar 

  109. Xie Y, Aiken A. Context- and path-sensitive memory leak detection. In: Proceedings of the 10th European Software Engineering Conference Held Jointly with 13th ACM SIGSOFT International Symposium on Foundations of Software Engineering. New York: ACM, 2005. 115–125

    Google Scholar 

  110. Gao Q, Xiong Y, Mi Y, et al. Safe memory-leak fixing for C programs. In: Proceedings of the 37th IEEE International Conference on Software Engineering (ICSE), Florence, 2015. 459–470

    Google Scholar 

  111. Lattner C, Lenharth A, Adve V. Making context-sensitive points-to analysis with heap cloning practical for the real world. In: Proceedings of the 28th ACM SIGPLAN Conference on Programming Language Design and Implementation. New York: ACM, 2007. 278–289

    Google Scholar 

  112. Hardekopf B, Lin C. Semi-sparse flow-sensitive pointer analysis. In: Proceedings of the 36th Annual ACM SIGPLANSIGACT Symposium on Principles of Programming Languages. New York: ACM, 2009. 226–238

    Google Scholar 

  113. Hardekopf B, Lin C. Flow-sensitive pointer analysis for millions of lines of code. In: Proceedings of the 9th Annual IEEE/ACM International Symposium on Code Generation and Optimization (CGO), Chamonix, 2011. 289–298

    Chapter  Google Scholar 

  114. Wang J, Ma X-D, Dong W, et al. Demand-driven memory leak detection based on flow- and context-sensitive pointer analysis. J Comput Sci Tech, 2009, 24: 347–356

    Article  Google Scholar 

  115. Kam J, Ullman J. Monotone data flow analysis frameworks. Act Inf, 1977, 7: 305–317

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Key Laboratory of High Confidence Software Technologies, MoE Institute of Software, School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

    Qing Gao, Jun Li, Yingfei Xiong, Dan Hao & Lu Zhang

  2. NEC Laboratories America, Inc., Princeton, NJ, 08540, USA

    Xusheng Xiao

  3. Accenture Technology Labs, San Jose, CA, 95113, USA

    Kunal Taneja

  4. Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA

    Tao Xie

Authors
  1. Qing Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yingfei Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dan Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xusheng Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kunal Taneja
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Tao Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yingfei Xiong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, Q., Li, J., Xiong, Y. et al. High-confidence software evolution. Sci. China Inf. Sci. 59, 071101 (2016). https://doi.org/10.1007/s11432-016-5572-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-016-5572-2

Keywords

Search

Navigation