Journal of Software:2017.28(6):1488-1497

(School of Computing and Communications, The Open University, Milton Keynes MK76AA;河南大学 计算机与信息工程学院, 河南 开封 475001)
Little Model in Big Data: An Algebraic Approach to Analysing Abstract Software Behaviours
YU Yi-Jun,LIU Chun
(School of Computing and Communications, The Open University, Milton Keynes MK76 AA;School of Computer and Information Engineering, He'nan University, Kaifeng 475001, China)
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Received:October 09, 2016    Revised:October 26, 2016
> 中文摘要: 问题框架方法分析软件需求时需要通过借助领域知识及其之间的结构关系来论述用户的需求是可以被软件系统满足的.这类定性的可满足性论述支持早期需求决策,选择合理的软件体系结构和设计方案.但是,当前的移动软件需求方是偏好各异的用户个体,需求差异化明显,而且根据应用场景,这些需求会动态地发生变化.在这种情况下,现有的定性分析方法不再适用.大数据分析提供一种数据驱动的深度学习机制,为很多实践者采用.但依靠数据驱动的软件分析往往就事论事,仍然不能从根本上提供一个合理的论述来说明大量软件用户的需求到底是什么,也无法对可信软件的安全性和私密性提供可靠的论证.再多的数据也只能提供统计意义的表象,而无法彻底防范借用漏洞的攻击.尝试从提炼软件抽象目标行为的角度进一步深化问题框架的研究思路,针对各类个体行为建立概率模型,提出一种基于模型代数分析的方法,以避开纯粹数据驱动思路的大数据分析盲点.通过对安全和隐私性问题的分析,对所提出的方法可用性及局限性进行探讨,对未来大数据软件需求研究给予一定的启示.
Abstract:The problem frame method typically uses domain knowledge in order to demonstrate that a software system can satisfy the requirements of stakeholders by specifying how machine relates to stakeholders' problems. Qualitatively, satisfiability discourse can guide a software engineer to make early decisions on what the right solution is to the right problem. However, mobile apps deployed to app stores often not only need to accommodate millions of individual users whose requirements have subtle differences, but also may change at runtime under varying application contexts. Requirements of such apps can no longer be analyzed qualitatively to cover all situations. Big data analysis through deep learning has been increasingly adopted in practice to replace deep requirements analysis. Although effective in making statistically sound decisions, the conclusions of pure big data analysis are merely a set of unexplainable parameters, which cannot be used to show that individual users' requirements are satisfied, nor can they reliably validate the trustworthiness and dependability in terms of security and privacy. After all, training with more datasets could only improve statistical significance, but cannot prevent software systems from the malicious exploitation of outliers. This paper attempts to follow Jackson's teaching of abstract goal behaviors as intermediate between requirements and software domains, and proposes an algebraic approach to analyzing the consequences of probabilistic software behavior models, so as to circumvent some blind spots of purely data-driven approaches. Through examples in security and privacy areas, the challenges and limitations to big data software requirement analysis are discussed.
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基金项目:欧洲研究理事会高级研究基金(291652);国家自然科学基金(61300035) 欧洲研究理事会高级研究基金(291652);国家自然科学基金(61300035)
Foundation items:European Research Council Advanced Grant (291652); National Natural Science Foundation of China (61300035)
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YU Yi-Jun,LIU Chun.Little Model in Big Data: An Algebraic Approach to Analysing Abstract Software Behaviours.Journal of Software,2017,28(6):1488-1497