SoftwareDynamics²: Fine-Grained Evolution of Software Behavior

软件系统不断发展。虽然软件系统的静态特性的演化，例如由软件度量、代码克隆和静态依赖所表示的，已经被详细地研究过，但是动态特性的演化只在宏观层面上被研究过。软件开发人员和研究人员目前还不容易回答一些基本问题，例如，特定代码更改对软件系统执行的影响是什么，或者哪些代码更改导致了执行时间的降低。这个由斯图加特大学的Fabian Beck和来自阿尔彭-阿德里亚大学的Martin Pinzger合作的项目旨在调查软件进化和软件执行之间的相互作用。为此，我们建议研究新的方法和技术来分析和可视化特定代码更改对软件系统动态行为的影响，并找到软件系统演化中动态行为特定更改的原因。与之前的研究相比，我们将在细粒度的细节层面上分析软件进化和软件执行：我们将在抽象语法树的层面上考虑源代码的变化，并在调用和对象图的层面上观察行为的变化。我们将在一系列不同软件系统的实证研究和软件开发人员的用户研究中评估我们的方法和技术。此外，我们将把这些方法集成到新的预测和推荐技术中，以帮助软件开发人员优化软件系统的特定性能指标，例如运行时或内存消耗。本研究项目的一个主要挑战是将单个代码更改映射到动态行为中的个体差异。此外，所有的方法都需要是可伸缩的，并支持多个层次的细节，以使细粒度的数据对软件开发人员和研究人员来说是可探索和可理解的。对两个时间维度的系统细致的分析创造了一个独特的研究机会，这是研究者尚未探索过的。该项目的计划成果承诺为研究人员提供方法，以更好地理解软件系统静态属性之外的软件进化，并为开发人员提供简化软件维护的方法，特别是优化软件系统的动态行为。

Software systems continuously evolve. While the evolution of static properties of software systems, such as represented by software metrics, code clones, and static dependencies, has been studied in detail, the evolution of dynamic properties has been investigated only at the macroscopic level. Basic questions, such as, what are the effects of a particular code change on the execution of a software system, or, which code changes caused the degradation of the execution time, are currently not easy to answer by software developers and researchers.This joint project between Fabian Beck from the University of Stuttgart and Martin Pinzger from the Alpen-Adria University Klagenfurt targets the investigation of the interplay between software evolution and software execution. To this end, we propose to research novel methods and techniques to analyze and visualize the impact of specific code changes on the dynamic behavior of a software system, and to find causes for specific changes of dynamic behavior in the evolution of a software system. In contrast to previous research, we will analyze both, software evolution and software execution, on a fine-grained level of detail: we will consider changes in the source code on the level of abstract syntax trees and observe changes in the behavior down to the level of call and object graphs. We will evaluate our methods and techniques in a number of empirical studies with diverse software systems and in user studies with software developers. Furthermore, we will integrate the methods into novel prediction and recommendation techniques to assist software developers in optimizing specific performance metrics of a software system, such as runtime or memory consumption. A major challenge of this research project will be the mapping of single code changes to individual differences in the dynamic behavior. In addition, all methods need to be scalable and support multiple levels of details to make the fine-grained data explorable and understandable to software developers and researchers.The systematic and detailed analysis of the two time dimensions creates a unique research opportunity that has not yet been explored by researchers. The planned outcomes of the project promise to provide methods for researchers to gain a better understanding of software evolution beyond static properties of software systems, and for developers to ease software maintenance, in particular, optimizing the dynamic behavior of software systems.