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Empirical research on the impact and evolution of Bad Smells

恶臭影响及演变的实证研究

基本信息

批准号：
242915193
负责人：
Professor Dr. Rainer Koschke
金额：
--
依托单位：
Technologie-Zentrum Informatik und Informationstechnik (TZI)
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2014
资助国家：
德国
起止时间：
2013-12-31 至 2017-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/242915193?language=en
关键词：
Empirical research impact evolution Bad

项目摘要

Bad Smells are code and design anomalies that have a negative effect on the comprehensibility and changeability of software and thus hinder its evolution. These bad programming and design practices may at some point lead to a state of the software where it is no longer economical to make a change and thus shorten the life time of software. If systems are to be long living, Bad Smells must be detected, traced, and handled - in other words, actively managed. A lot of research on software quality is devoted to Bad Smells. There are even software quality certifications such as the Software Quality Index or Trusted Product Maintainability offered by the German TÜVit that are based on occurrences of Bad Smells. These range from duplicated code, high coupling, low cohesion to excessive size and many other code aspects. Nevertheless, the true impact of Bad Smells is not yet sufficiently investigated nor is it empirically proven. Consequently, it is not fully clear, whether, how, and what kind of Bad Smells impact maintenance negatively. In addition to that, little is known how Bad Smells evolve. Most studies typically look only at a single snapshot of the software system under study and ignore evolutionary data.Our proposed project aims at comprehensively capturing Bad Smells in software, with a strong focus on their evolution. We search for re-ocurring patterns and relations in the collected data on Bad Smells. We investigate whether evolutionary data help to better define, detect, understand, and assess Bad Smells and evaluate whether and how they are suitable to assess the internal software quality. We disseminate our data among the research community as a basis for further complementary and comparative studies. Our long-term research aims at empirically-founded maintainability models to support the evolution of long-living systems.

坏味道是代码和设计异常，对软件的可理解性和可更改性产生负面影响，从而阻碍其发展。这些糟糕的编程和设计实践可能会在某些时候导致软件的状态，其中进行更改不再经济，从而缩短软件的生命周期。如果系统要长寿，必须检测、跟踪和处理不良气味-换句话说，积极管理。很多关于软件质量的研究都致力于坏味道。甚至还有软件质量认证，如德国TÜVit提供的软件质量指数或可信产品可维护性，这些认证都是基于不良气味的发生。这些问题包括重复的代码、高耦合、低内聚、过大以及许多其他代码方面。然而，臭味的真正影响尚未得到充分的研究，也没有经验证明。因此，目前还不完全清楚，是否，如何以及什么样的不良气味会对保养产生负面影响。除此之外，我们对臭味是如何演变的知之甚少。大多数研究通常只关注所研究的软件系统的单个快照，而忽略了进化数据。我们提出的项目旨在全面捕获软件中的坏气味，并重点关注它们的进化。我们在收集到的关于难闻气味的数据中寻找重复发生的模式和关系。我们调查进化数据是否有助于更好地定义，检测，理解和评估坏气味，并评估它们是否以及如何适合评估内部软件质量。我们在研究界传播我们的数据，作为进一步补充和比较研究的基础。我们的长期研究旨在建立可维护性模型，以支持长寿系统的发展。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Effect of Clone Information on the Performance of Developers Fixing Cloned Bugs

克隆信息对开发人员修复克隆错误的性能的影响

DOI：
10.1109/scam.2014.10
发表时间：
2014
期刊：
2014 IEEE 14th International Working Conference on Source Code Analysis and Manipulation
影响因子：
0
作者：
Bazrafshan;Saman ;Koschke;Rainer
通讯作者：
Rainer

An arc-based approach for visualization of code smells

一种基于弧线的代码异味可视化方法