一句话：提出面向状态系统的模型级变更影响分析方法，自动计算 EFSM 变更影响的下界与上界。
软件维护中，实施变更前进行影响分析至关重要；现有工具多度量代码级影响，开发早期模型级影响量化研究较少。本文面向电信、嵌入式等状态类系统，提出基于模型的变更影响分析方法：利用模型依赖自动度量请求变更的预期影响，不依赖维护者经验，并生成影响下界与上界两个集合。方法主要针对扩展有限状态机（EFSM），也可扩展到其他行为模型。作者在六个 EFSM 模型上实证评估：单个变迁修改的影响规模平均占模型总规模的 14%–38%，多变迁修改占 30%–64%。研究还分析模型结构与影响集大小的相关性，定义模型密度和数据密度，并发现其可能是影响集大小的主要因素。
Software maintenance is becoming more challenging with the increased complexity of the software and the frequently applied changes. Performing impact analysis before the actual implementation of a change is a crucial task during system maintenance. While many tools and techniques are available to measure the impact of a change at the code level, only a few research work is done to measure the impact of a change at an earlier stage in the development process. Measuring the impact of a change at the model level speeds up the maintenance process allowing early discovery of critical components of the system before applying the actual change at the code level. In this paper, we present model-based impact analysis approach for state-based systems such as telecommunication or embedded systems. The proposed approach uses model dependencies to automatically measure the expected impact for a requested change instead of relying on the expertise of system maintainers, and it generates two impact sets representing the lower bound and the upper bound of the impact. Although it can be extended to other behavioral models, the presented approach mainly addresses extended finite-state machine (EFSM) models. An empirical study is conducted on six EFSM models to investigate the usefulness of the proposed approach. The results show that on average the size of the impact after a single modification (a change in a one EFSM transition) ranges between 14 and 38 % of the total size of the model. For a modification involving multiple transitions, the average size of the impact ranges between 30 and 64 % of the total size of the model. Additionally, we investigated the relationships (correlation) between the structure of the EFSM model, and the size of the impact sets. Upon preliminary analysis of the correlation, the concepts of model density and data density were defined, and it was found that they could be the major factors influencing the sizes of impact sets for models. As a result, these factors can be used to determine the types of models for which the proposed approach is the most appropriate.
DOI 原文 ·
@inproceedings{paperbot4699,
title = {Automatically quantifying the impact of a change in systems (journal-first abstract)},
author = {Nada Almasri and Luay Tahat and Bogdan Korel},
booktitle = {ASE},
year = {2018},
doi = {10.1145/3238147.3241984}
}