微信服务号

微信订阅号

2025年5月1日 19:59 星期四
首页 > 过刊浏览>2018年第29卷第6期 >1716-1738. DOI:10.13328/j.cnki.jos.005541
PDF HTML阅读 XML下载 导出引用 引用提醒
API使用的关键问题研究
作者:
作者简介:

李正(1985-),男,天津人,博士生,主要研究领域为软件工程,系统安全;吴敬征(1982-),男,博士,副研究员,主要研究领域为系统安全,漏洞挖掘,移动安全;李明树(1965-),男,博士,研究员,博士生导师,CCF会士,主要研究领域为操作系统深度设计,可信软件过程以及基础软硬件核心技术与应用.

通讯作者:

李明树,E-mail:mingshu@admin.iscas.ac.cn

基金项目:

国家科技重大专项（2014ZX01029101-002）；国家自然科学基金（61772507）


Study on Key Issues in API Usage
Author:
Fund Project:

National Science and Technology Major Project (2014ZX01029101-002); National Natural Science Foundation ofChina (61772507)

  • 摘要
    • | |
  • 访问统计
    • |
  • 参考文献 [138]
    • |
  • 相似文献
    • |
  • 引证文献
    • | |
  • 文章评论
    摘要:

    API （application programming interface，应用程序编程接口）在现代软件开发过程中被广泛使用.开发人员通过调用API快速构建项目，节省了大量的时间.但由于API数量众多、文档不够完善、维护更新不及时等原因，开发人员在学习使用API的过程中面临着严峻的挑战.一旦API使用不正确，程序可能会出现缺陷甚至严重的安全问题.通过对API相关文献的深入调研，对近些年来国内外学者在该研究领域取得的成果进行了系统总结.首先，介绍了API的基本概念并分析出影响API使用的3个关键问题：API文档质量不高、调用规约不完整以及API调用序列难以确定；接着，从API文档、调用规约和API推荐这3个主要方面对研究成果进行全面的分析；最后，对未来研究可能面临的挑战进行了展望.

    Abstract:

    API (application programming interface) is widely used in modern software development process. Developers save a lot of time when they quickly build projects through invoking APIs. However, API is often difficult to use for many reasons, such as the presence of large number of interfaces, lack of perfect document, and no timely maintenance and updates. Further, API is often used incorrectly, resulting in bugs and sometimes significant security problems.This paper summarizes the recent domestic and overseas research results based on a thorough survey of the API related literatures. Firstly, it introduces the API concept and recognizes the three key issues that affect the API usage:poor API documentation, incomplete invocation specification and undetermined API call sequence. Next, it analyzes the latest advances from these three main aspects:API document, invocation specification and API recommendation. Finally, this paper outlines the challenges of the future research.

    参考文献
    [1] Ko AJ, Myers BA, Coblenz MJ, Aung HH. An exploratory study of how developers seek, relate, and collect relevant information during software maintenance tasks. IEEE Trans. on Software Engineering, 2006,32(12):971-987.[doi:10.1109/tse.2006.116]
    [2] Nadi S, Kr S, Mezini M, Bodden E. Jumping through hoops:Why do Java developers struggle with cryptography APIs? In:Proc. of the 38th Int'l Conf. on Software Engineering. New York:ACM Press, 2016. 935-946.[doi:10.1145/2884781.2884790]
    [3] Robillard MP, Deline R. A field study of API learning obstacles. Empirical Software Engineering, 2011,16(6):703-732.[doi:10.1007/s10664-010-9150-8]
    [4] Ko AJ, Myers BA, Aung HH. Six learning barriers in end-user programming systems. In:Proc. of the 2004 IEEE Symp. on Visual Languages-Human Centric Computing. Washington:IEEE Computer Society, 2004. 199-206.[doi:10.1109/vlhcc.2004.47]
    [5] Lazar D, Chen H, Wang X, Zeldovich N. Why does cryptographic software fail? A case study and open problems. In:Proc. of 5th Asia-Pacific Workshop on Systems. New York:ACM Press, 2014. 1-7.[doi:10.1145/2637166.2637237]
    [6] Egele M, Brumley D, Fratantonio Y, Kruegel C. An empirical study of cryptographic misuse in android applications. In:Proc. of the 2013 ACM SIGSAC Conf. on Computer & Communications Security. New York:ACM Press, 2013. 73-84.[doi:10.1145/2508859.2516693]
    [7] Georgiev M, Iyengar S, Jana S, Anubhai R, Boneh D, Shmatikov V. The most dangerous code in the world:Validating SSL certificates in non-browser software. In:Proc. of the 2012 ACM Conf. on Computer and Communications Security. New York:ACM Press, 2012. 38-49.[doi:10.1145/2382196.2382204]
    [8] Fahl S, Harbach M, Perl H, Koetter M, Smith M. Rethinking SSL development in an appified world. In:Proc. of the 2013 ACM SIGSAC Conf. on Computer & Communications Security. New York:ACM Press, 2013. 49-60.[doi:10.1145/2508859.2516655]
    [9] Parnas DL, Madey J, Iglewski M. Precise documentation of well-structured programs. IEEE Trans. on Software Engineering, 1994, 20(12):948-976.[doi:10.1109/32.368133]
    [10] Saied MA, Sahraoui H, Dufour B. An observational study on API usage constraints and their documentation. In:Proc. of the 2015 IEEE 22nd Int'l Conf. on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2015. 33-42.[doi:10.1109/SANER. 2015.7081813]
    [11] Maalej W, Robillard MP. Patterns of knowledge in API reference documentation. IEEE Trans. on Software Engineering, 2013, 39(9):1264-1282.[doi:10.1109/tse.2013.12]
    [12] Sacramento P, Cabral B, Marques P. Unchecked exceptions:Can the programmer be trusted to document exceptions. In:Proc. of the 2nd Int'l Conf. on Innovative Views of.NET Technologies. 2006.
    [13] Rubio-Gonz C, Liblit B. Expect the unexpected:Error code mismatches between documentation and the real world. In:Proc. of the 9th ACM SIGPLAN-SIGSOFT Workshop on Program Analysis for Software Tools and Engineering, Vol.1806687. ACM Press, 2010. 73-80.[doi:10.1145/1806672.1806687]
    [14] Kim J, Lee S, Hwang SW, Kim S. Enriching documents with examples:A corpus mining approach. ACM Trans. on Information Systems, 2013, 31(1):1-27.[doi:10.1145/2414782.2414783]
    [15] Subramanian S, Inozemtseva L, Holmes R. Live API documentation. In:Proc. of the 36th Int'l Conf. on Software Engineering. New York:ACM Press, 2014. 643-652.[doi:10.1145/2568225.2568313]
    [16] Treude C, Robillard MP. Augmenting API documentation with insights from stack overflow. In:Proc. of the 38th Int'l Conf. on Software Engineering. New York:ACM Press, 2016. 392-403.[doi:10.1145/2884781.2884800]
    [17] Chen C, Zhang K. Who asked what:Integrating crowdsourced FAQs into API documentation. In:Proc. of the 36th Int'l Conf. on Software Engineering. New York:ACM Press, 2014. 456-459.[doi:10.1145/2591062.2591128]
    [18] Zhou J, Walker RJ. API deprecation:A retrospective analysis and detection method for code examples on the Web. In:Proc. of the 201624th ACM SIGSOFT Int'l Symp. on Foundations of Software Engineering. New York:ACM Press, 2016. 266-277.[doi:10.1145/2950290.2950298]
    [19] Zhou Y, Gu R, Chen T, Huang Z, Panichella S, Gall H. Analyzing APIs documentation and code to detect directive defects. In:Proc. of the 39th Int'l Conf. on Software Engineering. Piscataway:IEEE Press, 2017. 27-37.[doi:10.1109/icse.2017.11]
    [20] Zhong H, Su Z. Detecting API documentation errors. ACM SIGPLAN Notices, 2013,48(10):803-816.[doi:10.1145/2544173. 2509523]
    [21] Dekel U, Herbsleb JD. Improving API documentation usability with knowledge pushing. In:Proc. of the 31st Int'l Conf. on Software Engineering. Washington:IEEE Computer Society, 2009. 320-330.[doi:10.1109/icse.2009.5070532]
    [22] Monperrus M, Eichberg M, Tekes E, Mezini M. What should developers be aware of? An empirical study on the directives of API documentation. Empirical Software Engineering, 2012,17(6):703-737.[doi:10.1007/s10664-011-9186-4]
    [23] Petrosyan G, Robillard MP, Mori RD. Discovering information explaining API types using text classification. In:Proc. of the 37th Int'l Conf. on Software Engineering-Vol.1. Piscataway:IEEE Press, 2015. 869-879.
    [24] Jiang H, Zhang J, Ren Z, Zhang T. An unsupervised approach for discovering relevant tutorial fragments for APIs. In:Proc. of the 39th Int'l Conf. on Software Engineering. Piscataway:IEEE Press, 2017. 38-48.[doi:10.1109/icse.2017.12]
    [25] Parnas DL. Precise documentation:The key to better software. In:Proc. of the Future of Software Engineering. Berlin:Springer-Verlag, 2011. 125-148.
    [26] Bajracharya S, Lopes C. Mining search topics from a code search engine usage log. In:Proc. of the 20096th IEEE Int'l Working Conf. on Mining Software Repositories. Washington:IEEE Computer Society, 2009. 111-120.[doi:10.1109/msr.2009.5069489]
    [27] Dagenais B, Robillard MP. Creating and evolving developer documentation:Understanding the decisions of open source contributors. In:Proc. of the 18th ACM SIGSOFT Int'l Symp. on Foundations of Software Engineering. New York:ACM Press, 2010. 127-136.[doi:10.1145/1882291.1882312]
    [28] Dagenais B, Robillard MP. Recovering traceability links between an API and its learning resources. In:Proc. of the 34th Int'l Conf. on Software Engineering. Piscataway:IEEE Press, 2012. 47-57.
    [29] Rigby PC, Robillard MP. Discovering essential code elements in informal documentation. In:Proc. of the 2013 Int'l Conf. on Software Engineering. Piscataway:IEEE Press, 2013. 832-841.
    [30] Antoniol G, Canfora G, Casazza G, Lucia AD, Merlo E. Recovering traceability links between code and documentation. IEEE Trans. on Software Engineering, 2002,28(10):970-983.[doi:10.1109/tse.2002.1041053]
    [31] Duala-Ekoko E, Robillard MP. Asking and answering questions about unfamiliar APIs:An exploratory study. In:Proc. of the 34th Int'l Conf. on Software Engineering. Piscataway:IEEE Press, 2012. 266-276.
    [32] Shi L, Zhong H, Xie T, Li M. An empirical study on evolution of API documentation. In:Proc. of the 14th Int'l Conf. on Fundamental Approaches to Software Engineering:Part of the Joint European Conf. on Theory and Practice of Software. Berlin:Springer-Verlag, 2011. 416-431.
    [33] Parnin C, Treude C. Measuring API documentation on the Web. In:Proc. of the 2nd Int'l Workshop on Web 2.0 for Software Engineering. New York:ACM Press, 2011. 25-30.[doi:10.1145/1984701.1984706]
    [34] Cook JE, Wolf AL. Discovering models of software processes from event-based data. ACM Trans. on Software Engineering and Methodology, 1998,7(3):215-249.[doi:10.1145/287000.287001]
    [35] Dallmeier V, Lindig C, Wasylkowski A, Zeller A. Mining object behavior with ADABU. In:Proc. of the 2006 Int'l Workshop on Dynamic Systems Analysis. New York:ACM Press, 2006. 17-24.[doi:10.1145/1138912.1138918]
    [36] Pradel M, Gross TR. Automatic generation of object usage specifications from large method traces. In:Proc. of the 2009 IEEE/ACM Int'l Conf. on Automated Software Engineering. Washington:IEEE Computer Society, 2009. 371-382.[doi:10.1109/ase. 2009.60]
    [37] Li Z, Zhou Y. PR-Miner:Automatically extracting implicit programming rules and detecting violations in large software code. SIGSOFT Software Engineering Notes, 2005,30(5):306-315.[doi:10.1145/1095430.1081755]
    [38] Livshits B, Zimmermann T. DynaMine:Finding common error patterns by mining software revision histories. SIGSOFT Software Engineering Notes, 2005,30(5):296-305.[doi:10.1145/1095430.1081754]
    [39] Yang J, Evans D, Bhardwaj D, Bhat T, Das M. Perracotta:Mining temporal API rules from imperfect traces. In:Proc. of the 28th Int'l Conf. on Software Engineering. New York:ACM Press, 2006. 282-291.[doi:10.1145/1134285.1134325]
    [40] Thummalapenta S, Xie T. Alattin:Mining alternative patterns for defect detection. Automated Software Engineering, 2011,18(3):293-323.[doi:10.1007/s10515-011-0086-z]
    [41] Liang B, Bian P, Zhang Y, Shi W, You W, Cai Y. AntMiner:Mining more bugs by reducing noise interference. In:Proc. of the 38th Int'l Conf. on Software Engineering. New York:ACM Press, 2016. 333-344.[doi:10.1145/2884781.2884870]
    [42] Murali V, Chaudhuri S, Jermaine C. Bayesian specification learning for finding API usage errors. In:Proc. of the 201711th Joint Meeting on Foundations of Software Engineering. New York:ACM Press, 2017. 151-162.[doi:10.1145/3106237.3106284]
    [43] Gabel M, Su Z. Symbolic mining of temporal specifications. In:Proc. of the 30th Int'l Conf. on Software Engineering. New York:ACM Press, 2008. 51-60.[doi:10.1145/1368088.1368096]
    [44] Gabel M, Su Z. Javert:fully automatic mining of general temporal properties from dynamic traces. In:Proc. of the 16th ACM SIGSOFT Int'l Symp. on Foundations of Software Engineering. New York:ACM Press, 2008. 339-349.[doi:10.1145/1453101. 1453150]
    [45] Gabel M, Su Z. Online inference and enforcement of temporal properties. In:Proc. of the 32nd ACM/IEEE Int'l Conf. on Software Engineering-Vol.1. New York:ACM Press, 2010. 15-24.[doi:10.1145/1806799.1806806]
    [46] Zhong H, Zhang L, Xie T, Mei H. Inferring resource specifications from natural language API documentation. In:Proc. of the 2009 IEEE/ACM Int'l Conf. on Automated Software Engineering. Washington:IEEE Computer Society, 2009. 307-318.[doi:10.1109/ase.2009.94]
    [47] Nguyen TT, Nguyen HA, Pham NH, Al-Kofahi JM, Nguyen TN. Graph-Based mining of multiple object usage patterns. In:Proc. of the the 7th Joint Meeting of the European Software Engineering Conf. and the ACM SIGSOFT Symp. on The Foundations of Software Engineering. New York:ACM Press, 2009. 383-392.[doi:10.1145/1595696.1595767]
    [48] Wasylkowski A, Zeller A. Mining temporal specifications from object usage. In:Proc. of the 2009 IEEE/ACM Int'l Conf. on Automated Software Engineering. Washington:IEEE Computer Society, 2009. 295-306.[doi:10.1109/ase.2009.30]
    [49] Wei Y, Furia CA, Kazmin N, Meyer B. Inferring better contracts. In:Proc. of the 33rd Int'l Conf. on Software Engineering. New York:ACM Press, 2011. 191-200.[doi:10.1145/1985793.1985820]
    [50] Nguyen HA, Dyer R, Nguyen TN, Rajan H. Mining preconditions of APIs in large-scale code corpus. In:Proc. of the 22nd ACM SIGSOFT Int'l Symp. on Foundations of Software Engineering. New York:ACM Press, 2014. 166-177.[doi:10.1145/2635868. 2635924]
    [51] Henkel J, Reichenbach C, Diwan A. Discovering documentation for Java container classes. IEEE Trans. on Software Engineering, 2007,33(8):526-543.[doi:10.1109/tse.2007.70705].
    [52] Ramanathan MK, Grama A, Jagannathan S. Path-Sensitive inference of function precedence protocols. In:Proc. of the 29th Int'l Conf. on Software Engineering. Washington:IEEE Computer Society, 2007. 240-250.[doi:10.1109/icse.2007.63]
    [53] Lorenzoli D, Mariani L, Pezzè M. Automatic generation of software behavioral models. In:Proc. of the 30th Int'l Conf. on Software engineering. New York:ACM Press, 2008. 501-510.[doi:10.1145/1368088.1368157]
    [54] Krka I, Brun Y, Medvidovic N. Automatic mining of specifications from invocation traces and method invariants. In:Proc. of the 22nd ACM SIGSOFT Int'l Symp. on Foundations of Software Engineering. New York:ACM Press, 2014. 178-189.[doi:10.1145/2635868.2635890]
    [55] Dwyer MB, Avrunin GS, Corbett JC. Patterns in property specifications for finite-state verification. In:Proc. of the 21st Int'l Conf. on Software Engineering. New York:ACM Press, 1999. 411-420.[doi:10.1145/302405.302672]
    [56] Robillard MP, Bodden E, Kawrykow D, Mezini M, Ratchford T. Automated API property inference techniques. IEEE Trans. on Software Engineering, 2013,39(5):613-637.[doi:10.1109/tse.2012.63]
    [57] Engler D, Chen DY, Hallem S, Chou A, Chelf B. Bugs as deviant behavior:A general approach to inferring errors in systems code. SIGOPS Operating Systems Review, 2001,35(5):57-72.[doi:10.1145/502059.502041]
    [58] Qu JF, Liu M. Mining frequent itemsets using node-sets of a prefix-tree. In:Proc. of the 23rd Int'l Conf. on Database and Expert Systems Applications (DEXA 2012). Part I. Vienna, Berlin, Heidelberg:Springer-Verlag, 2012. 453-467.[doi:10.1007/978-3-642-32600-4_34]
    [59] Agrawal R, Srikant R. Fast algorithms for mining association rules in large databases. In:Proc. of the 20th Int'l Conf. on Very Large Data Bases. San Francisco:Morgan Kaufmann Publishers Inc., 1994. 487-499.
    [60] Kagdi H, Collard ML, Maletic JI. Comparing approaches to mining source code for call-usage patterns. In:Proc. of the 4th Int'l Workshop on Mining Software Repositories. Washington:IEEE Computer Society, 2007. 20.[doi:10.1109/msr.2007.3]
    [61] Kagdi H, Collard ML, Maletic JI. An approach to mining call-usage patternswith syntactic context. In:Proc. of the 22nd IEEE/ACM Int'l Conf. on Automated Software Engineering. New York:ACM Press, 2007. 457-460.[doi:10.1145/1321631.1321708]
    [62] Thummalapenta S, Xie T. Mining exception-handling rules as sequence association rules. In:Proc. of the 31st Int'l Conf. on Software Engineering. Washington:IEEE Computer Society, 2009. 496-506.[doi:10.1109/icse.2009.5070548]
    [63] Lo D, Khoo SC. SMArTIC:Towards building an accurate, robust and scalable specification miner. In:Proc. of the 14th ACM SIGSOFT Int'l Symp. on Foundations of Software Engineering. New York:ACM Press, 2006. 265-275.[doi:10.1145/1181775. 1181808]
    [64] Lorenzoli D, Mariani L, Pezz M, #232. Automatic generation of software behavioral models. In:Proc. of the 30th Int'l Conf. on Software Engineering. New York:ACM Press, 2008. 501-510.[doi:10.1145/1368088.1368157]
    [65] Walkinshaw N, Bogdanov K. Inferring finite-state models with temporal constraints. In:Proc. of the 200823rd IEEE/ACM Int'l Conf. on Automated Software Engineering. Washington:IEEE Computer Society, 2008. 248-257.[doi:10.1109/ase.2008.35]
    [66] Ammons G, Bod R, #237, Larus JR. Mining specifications. SIGPLAN Notices, 2002,37(1):4-16.[doi:10.1145/565816.503275]
    [67] Chang RY, Podgurski A, Yang J. Finding what's not there:A new approach to revealing neglected conditions in software. In:Proc. of the 2007 Int'l Symp. on Software Testing and Analysis. New York:ACM Press, 2007. 163-173.[doi:10.1145/1273463. 1273486]
    [68] Zhong H, Zhang L, Mei H. Inferring specifications of object oriented APIs from API source code. In:Proc. of the 15th Asia-Pacific Software Engineering Conf. (APSEC 2008). IEEE, 2008. 221-228.[doi:10.1109/APSEC.2008.54]
    [69] Meyer B. Applying "Design by Contract". Computer, 1992,25(10):40-51.[doi:10.1109/2.161279]
    [70] Ernst MD, Cockrell J, Griswold WG, Notkin D. Dynamically discovering likely program invariants to support program evolution. In:Proc. of the 21st Int'l Conf. on Software Engineering. New York:ACM Press, 1999. 213-224.[doi:10.1145/302405.302467]
    [71] Csallner C, Tillmann N, Smaragdakis Y. DySy:Dynamic symbolic execution for invariant inference. In:Proc. of the 30th Int'l Conf. on Software Engineering. New York:ACM Press, 2008. 281-290.[doi:10.1145/1368088.1368127]
    [72] Ernst MD, Perkins JH, Guo PJ, McCamant S, Pacheco C, Tschantz MS, Xiao C. The Daikon system for dynamic detection of likely invariants. Science of Computer Programming, 2007,69(1-3):35-45.[doi:10.1016/j.scico.2007.01.015]
    [73] Flanagan C, Leino KRM. Houdini, an annotation assistant for ESC/Java. In:Proc. of the Int'l Symp. of Formal Methods Europe on Formal Methods for Increasing Software Productivity. London:Springer-Verlag, 2001. 500-517.
    [74] Guttag JV, Horning JJ. The algebraic specification of abstract data types. Acta informatica, 1978,10(1):27-52.[doi:10.1007/bf 00260922]
    [75] Bagge AH, Haveraaen M. Specification of generic APIs, or:Why algebraic may be better than pre/post. Ada Letters, 2014,34(3):71-80.[doi:10.1145/2692956.2663183]
    [76] Legunsen O, Hassan WU, Xu X, Roşu G, Marinov D. How good are the specs? A study of the bug-finding effectiveness of existing Java API specifications. In:Proc. of the 31st IEEE/ACM Int'l Conf. on Automated Software Engineering. New York:ACM Press, 2016. 602-613.[doi:10.1145/2970276.2970356]
    [77] Thung F. API recommendation system for software development. In:Proc. of the 31st IEEE/ACM Int'l Conf. on Automated Software Engineering. New York:ACM Press, 2016. 896-899.[doi:10.1145/2970276.2975940]
    [78] Teyton C, Falleri JR, Blanc X. Mining library migration graphs. In:Proc. of the 201219th Working Conf. on Reverse Engineering. Washington:IEEE Computer Society, 2012. 289-298.[doi:10.1109/wcre.2012.38]
    [79] Thung F, Lo D, Lawall J. Automated library recommendation. In:Proc. of the 201320th Working Conf. on Reverse Engineering (WCRE). 2013. 182-191.
    [80] Chen C, Xing Z. SimilarTech:Automatically recommend analogical libraries across different programming languages. In:Proc. of the 31st IEEE/ACM Int'l Conf. on Automated Software Engineering. New York:ACM Press, 2016. 834-839.[doi:10.1145/2970276.2970290]
    [81] Xie T, Pei J. MAPO:Mining API usages from open source repositories. In:Proc. of the 2006 Int'l Workshop on Mining Software Repositories. New York:ACM Press, 2006. 54-57.[doi:10.1145/1137983.1137997]
    [82] Hindle A, Barr ET, Su Z, Gabel M, Devanbu P. On the naturalness of software. In:Proc. of the 34th Int'l Conf. on Software Engineering. Piscataway:IEEE Press, 2012. 837-847.
    [83] Wang J, Dang Y, Zhang H, Chen K, Xie T, Zhang D. Mining succinct and high-coverage API usage patterns from source code. In:Proc. of the 10th Working Conf. on Mining Software Repositories. Piscataway:IEEE Press, 2013. 319-328.
    [84] Raychev V, Vechev M, Yahav E. Code completion with statistical language models. SIGPLAN Notices, 2014,49(6):419-428.[doi:10.1145/2666356.2594321]
    [85] Nguyen AT, Nguyen TN. Graph-Based statistical language model for code. In:Proc. of the 37th Int'l Conf. on Software Engineering-Vol.1. Piscataway:IEEE Press, 2015. 858-868.
    [86] Nguyen TT, Pham HV, Vu PM, Nguyen TT. Learning API usages from bytecode:A statistical approach. In:Proc. of the 38th Int'l Conf. on Software Engineering. New York:ACM Press, 2016. 416-427.[doi:10.1145/2884781.2884873]
    [87] Fowkes J, Sutton C. Parameter-Free probabilistic API mining across GitHub. In:Proc. of the 201624th ACM SIGSOFT Int'l Symp. on Foundations of Software Engineering. New York:ACM Press, 2016. 254-265.[doi:10.1145/2950290.2950319]
    [88] Gu X, Zhang H, Zhang D, Kim S. Deep API learning. In:Proc. of the 201624th ACM SIGSOFT Int'l Symp. on Foundations of Software Engineering. New York:ACM Press, 2016. 631-642.[doi:10.1145/2950290.2950334]
    [89] Zhang C, Yang J, Zhang Y, Fan J, Zhang X, Zhao J, Ou P. Automatic parameter recommendation for practical API usage. In:Proc. of the 34th Int'l Conf. on Software Engineering. Piscataway:IEEE Press, 2012. 826-836.
    [90] Asaduzzaman M, Roy CK, Monir S, Schneider KA. Exploring API method parameter recommendations. In:Proc. of the 2015 IEEE Int'l Conf. on Software Maintenance and Evolution (ICSME). Washington:IEEE Computer Society, 2015. 271-280.[doi:10.1109/icsm.2015.7332473]
    [91] Dig D, Johnson R. How do APIs evolve? a story of refactoring:Research articles. Journal of Software Maintenance and Evolution, 2006,18(2):83-107.[doi:10.1002/smr.v18:2]
    [92] Dagenais B, Robillard MP. Recommending adaptive changes for framework evolution. ACM Transactions on Software Engineering and Methodology, 2011,20(4):1-35.[doi:10.1145/2000799.2000805]
    [93] Zhong H, Thummalapenta S, Xie T, Zhang L, Wang Q. Mining API mapping for language migration. In:Proc. of the 32nd ACM/IEEE Int'l Conf. on Software Engineering-Vol.1. New York:ACM Press, 2010. 195-204.[doi:10.1145/1806799.1806831]
    [94] Nguyen TD, Nguyen AT, Phan HD, Nguyen TN. Exploring API embedding for API usages and applications. In:Proc. of the 39th Int'l Conf. on Software Engineering. Piscataway:IEEE Press, 2017. 438-449.[doi:10.1109/icse.2017.47]
    [95] Wang W, Godfrey MW. Detecting API usage obstacles:A study of iOS and Android developer questions. In:Proc. of the 10th Working Conf. on Mining Software Repositories. Piscataway:IEEE Press, 2013. 61-64.
    [96] Agrawal R, Srikant R. Mining sequential patterns. In:Proc. of the 11th Int'l Conf. on Data Engineering. Washington:IEEE Computer Society, 1995. 3-14.
    [97] Srikant R, Agrawal R. Mining sequential patterns:Generalizations and performance improvements. In:Proc. of the 5th Int'l Conf. on Extending Database Technology:Advances in Database Technology. London:Springer-Verlag, 1996. 3-17.
    [98] Pei J, Han J, Mortazavi-Asl B, Pinto H, Chen Q, Dayal U, Hsu MC. PrefixSpan:Mining sequential patterns efficiently by prefix-projected pattern growth. In:Proc. of the 17th Int'l Conf. on Data Engineering. IEEE, 2001. 215-224.
    [99] Zaki MJ. SPADE:An efficient algorithm for mining frequent sequences. Machine Learning, 2001,42(1-2):31-60.[doi:10.1023/a:1007652502315]
    [100] Ayres J, Flannick J, Gehrke J, Yiu T. Sequential PAttern mining using a bitmap representation. In:Proc. of the 8th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. New York:ACM Press, 2002. 429-435.[doi:10.1145/775047.775109]
    [101] Wang J, Han J. BIDE:Efficient mining of frequent closed sequences. In:Proc. of the 20th Int'l Conf. on Data Engineering. Washington:IEEE Computer Society, 2004. 79.
    [102] Tatti N, Vreeken J. The long and the short of it:Summarising event sequences with serial episodes. In:Proc. of the 18th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. New York:ACM Press, 2012. 462-470.[doi:10.1145/2339530. 2339606]
    [103] Lam HT, Mörchen F, Fradkin D, Calders T. Mining compressing sequential patterns. Statistical Analysis and Data Mining:The ASA Data Science Journal, 2014,7(1):34-52.
    [104] Rosenfeld R. Two decades of statistical language modeling:Where do we go from here? Proc. of the IEEE, 2000,88(8):1270-1278.
    [105] Nguyen AT, Hilton M, Codoban M, Nguyen HA, Mast L, Rademacher E, Nguyen TN, Dig D. API code recommendation using statistical learning from fine-grained changes. In:Proc. of the 201624th ACM SIGSOFT Int'l Symp. on Foundations of Software Engineering. New York:ACM Press, 2016. 511-522.[doi:10.1145/2950290.2950333]
    [106] Elman JL. Finding structure in time. Cognitive Science, 1990,14(2):179-211.
    [107] Mikolov T, Karafiát M, Burget L, Cernocký J, Khudanpur S. Recurrent neural network based language model. In:Proc. of the INTERSPEECH 2010, Conf. of the Int'l Speech Communication Association. 2010. 1045-1048.
    [108] Cho K,Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In:Proc. of the EMNLP. 2014. 1724-1734.
    [109] Pradel M, Heiniger S, Gross TR. Static detection of brittle parameter typing. In:Proc. of the 2012 Int'l Symp. on Software Testing and Analysis. New York:ACM Press, 2012. 265-275.[doi:10.1145/2338965.2336785]
    [110] Pradel M, Gross TR. Detecting anomalies in the order of equally-typed method arguments. In:Proc. of the 2011 Int'l Symp. on Software Testing and Analysis. New York:ACM Press, 2011. 232-242.[doi:10.1145/2001420.2001448]
    [111] Xing Z, Stroulia E. API-Evolution support with diff-CatchUp. IEEE Trans. on Software Engineering, 2007,33(12):818-836.[doi:10.1109/tse.2007.70747]
    [112] Dig D, Comertoglu C, Marinov D, Johnson R. Automated detection of refactorings in evolving components. In:Proc. of the 20th European Conf. on Object-Oriented Programming. Berlin:Springer-Verlag, 2006. 404-428.[doi:10.1007/11785477_24]
    [113] Schäfer T, Jonas J, Mezini M. Mining framework usage changes from instantiation code. In:Proc. of the 30th Int'l Conf. on Software Engineering. New York:ACM Press, 2008. 471-480.[doi:10.1145/1368088.1368153]
    [114] Roover CD, Lammel R, Pek E. Multi-Dimensional exploration of API usage. In:Proc. of the IEEE Int'l Conf. on Program Comprehension. IEEE, 2013. 152-161.[doi:10.1109/ICPC.2013.6613843]
    [115] McIlroy MD. Mass-Produced software components. In:Proc. of the 1st Int'l Conf. on Software Engineering. 1968. 88-98.
    [116] Parnas DL. On the criteria to be used in decomposing systems into modules. Communications of the ACM, 1972,15(12):1053-1058.[doi:10.1145/361598.361623]
    [117] Plauger PJ. The Standard C Library. Prentice Hall PTR, 1991. 17-415.
    [118] Tsai CC, Jain B, Abdul NA, Porter DE. A study of modern Linux API usage and compatibility:What to support when you're supporting. In:Proc. of the 11th European Conf. on Computer Systems. New York:ACM Press, 2016. 1-16.[doi:10.1145/2901318.2901341]
    [119] Atlidakis V, Andrus J, Geambasu R, Mitropoulos D, Nieh J. POSIX abstractions in modern operating systems:The old, the new, and the missing. In:Proc. of the 11th European Conf. on Computer Systems. New York:ACM Press, 2016. 1-17.[doi:10.1145/2901318.2901350]
    [120] Qiu D, Li B, Leung H. Understanding the API usage in Java. Information and Software Technology, 2016,73(C):81-100.[doi:10.1016/j.infsof. 2016.01.011]
    [121] Bissyandé TF, Réveillère L, Lawall JL, Muller G. Ahead of time static analysis for automatic generation of debugging interfaces to the Linux kernel. Automated Software Engineering, 2016,23(1):3-41.[doi:10.1007/s10515-014-0152-4]
    [122] Bissyandé TF, Réveillère L, Lawall JL, Muller G. Diagnosys:Automatic generation of a debugging interface to the Linux kernel. In:Proc. of the 27th IEEE/ACM Int'l Conf. on Automated Software Engineering. New York:ACM Press, 2012. 60-69.[doi:10.1145/2351676.2351686]
    [123] Jezek K, Dietrich J, Brada P. How Java APIs break-An empirical study. Information and Software Technology, 2015,65(C):129-146.[doi:10.1016/j.infsof.2015.02.014]
    [124] Lin Z, Zhong H, Chen Y, Zhao J. LockPeeker:Detecting latent locks in Java APIs. In:Proc. of the 31st IEEE/ACM Int'l Conf. on Automated Software Engineering. New York:ACM Press, 2016. 368-378.[doi:10.1145/2970276.2970355]
    [125] Businge J, Serebrenik A, van den Brand M. Analyzing the Eclipse API usage:Putting the developer in the loop. In:Proc. of the 201317th European Conf. on Software Maintenance and Reengineering (CSMR). IEEE, 2013. 37-46.[doi:10.1109/CSMR. 2013.14]
    [126] Businge J, Serebrenik A, van den Brand MGJ. Eclipse API usage:The good and the bad. Software Quality Journal, 2015,23(1):107-141.[doi:10.1007/s11219-013-9221-3]
    [127] Businge J, Brand MVD, Serebrenik A. Survival of Eclipse third-party plug-ins. In:Proc. of the 2012 IEEE Int'l Conf. on Software Maintenance (ICSM). Washington:IEEE Computer Society, 2012. 368-377.[doi:10.1109/icsm.2012.6405295]
    [128] McDonnell T, Ray B, Kim M. An empirical study of API stability and adoption in the android ecosystem. In:Proc. of the 2013 IEEE Int'l Conf. on Software Maintenance. Washington:IEEE Computer Society, 2013. 70-79.[doi:10.1109/icsm.2013.18]
    [129] Bavota G, Linares-Vasquez M, Bernal-Cardenas CE, Di Penta M, Oliveto R, Poshyvanyk D. The impact of API change-and fault-proneness on the user ratings of android apps. IEEE Trans. on Software Engineering, 2015,41(4):384-407.[doi:10.1109/TSE.2014. 2367027]
    [130] Li L, Bissyandé TF, Le Traon Y, Klein J. Accessing inaccessible android APIs:An empirical study. In:Proc. of the 2016 IEEE Int'l Conf. on Software Maintenance and Evolution (ICSME). IEEE, 2016. 411-422.
    [131] Han J, Kywe SM, Yan Q, Bao F, Deng R, Gao D, Li Y, Zhou J. Launching generic attacks on iOS with approved third-party applications. In:Proc. of the 11th Int'l Conf. on Applied Cryptography and Network Security. Berlin:Springer-Verlag, 2013. 272-289.[doi:10.1007/978-3-642-38980-1_17]
    [132] Wang T, Lu K, Lu L, Chung S, Lee W. Jekyll on iOS:When benign apps become evil. In:Proc. of the 22nd USENIX Conf. on Security. Berkeley:USENIX Association, 2013. 559-572.
    [133] Deng Z, Saltaformaggio B, Zhang X, Xu D. iRiS:Vetting private API abuse in iOS applications. In:Proc. of the 22nd ACM SIGSAC Conf. on Computer and Communications Security. New York:ACM Press, 2015. 44-56.[doi:10.1145/2810103.2813675]
    [134] Zibran MF, Eishita FZ, Roy CK. Useful, but usable? Factors affecting the usability of APIs. In:Proc. of the 201118th Working Conf. on Reverse Engineering. Washington:IEEE Computer Society, 2011. 151-155.[doi:10.1109/wcre.2011.26]
    [135] Ellis B, Stylos J, Myers B. The factory pattern in API design:A usability evaluation. In:Proc. of the 29th Int'l Conf. on Software Engineering. Washington:IEEE Computer Society, 2007. 302-312.[doi:10.1109/icse.2007.85]
    [136] Stylos J, Myers BA. The implications of method placement on API learnability. In:Proc. of the 16th ACM SIGSOFT Int'l Symp. on Foundations of Software Engineering. New York:ACM Press, 2008. 105-112.[doi:10.1145/1453101.1453117]
    [137] Wu W, Guéhéneuc YG, Antoniol G, Kim M. AURA:A hybrid approach to identify framework evolution. In:Proc. of the 32nd ACM/IEEE Int'l Conf. on Software Engineering-Vol.1. New York:ACM Press, 2010. 325-334.[doi:10.1145/1806799.1806848]
    [138] Iyer S, Konstas I, Cheung A, Zettlemoyer L. Summarizing source code using a neural attention model. In:Proc. of the Meeting of the Association for Computational Linguistics. 2016. 2073-2083.
    相似文献
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

李正,吴敬征,李明树. API使用的关键问题研究.软件学报,2018,29(6):1716-1738

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2017-09-18
  • 最后修改日期:2017-11-29
  • 在线发布日期: 2018-02-08
文章二维码
友情链接:中国科学院软件研究所中国计算机学会中国科学院国家自然科学基金委员会CCF数字图书馆Int'l J of Softw Info计算机系统应用
您是第19894302位访问者
版权所有：中国科学院软件研究所 京ICP备05046678号-3
地址：北京市海淀区中关村南四街4号,邮政编码：100190
电话：010-62562563 传真：010-62562533 Email：jos@iscas.ac.cn
技术支持：北京勤云科技发展有限公司

京公网安备 11040202500063号