SoftwareDynamics²: Fine-Grained Evolution of Software Behavior

软件系统不断发展。虽然已经详细研究了软件系统的静态属性的演化，例如由软件度量、代码克隆和静态依赖所表示的，但动态属性的演化仅在宏观级别上进行了研究。软件开发人员和研究人员目前并不容易回答一些基本的问题，例如，特定的代码更改对软件系统的执行有什么影响，或者哪些代码更改导致了执行时间的下降。这个由斯图加特大学的Fabian Beck和阿尔本-阿德里亚大学Klagenfut的Martin Pinzger联合开展的项目旨在调查软件演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。与以前的研究不同，我们将在细粒度的细节级别上分析软件演化和软件执行：我们将在抽象语法树的级别上考虑源代码的更改，并观察行为的更改，直到调用图和对象图的级别。我们将在对不同软件系统的大量经验研究以及与软件开发人员的用户研究中对我们的方法和技术进行评估。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，如运行时或内存消耗。该研究项目的一个主要挑战将是将单个代码更改映射到动态行为中的个体差异。此外，所有方法都需要具有可扩展性，并支持多层次的细节，以使细粒度数据可供软件开发人员和研究人员探索和理解，对这两个时间维度的系统和详细分析创造了一个独特的研究机会，这是研究人员尚未探索的。该项目的计划成果有望为研究人员提供方法，以更好地了解软件演化，超越软件系统的静态属性，并为开发人员简化软件维护，特别是优化软件系统的动态行为提供方法。

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.