Empirical research on the impact and evolution of Bad Smells

不良嗅觉是指代码和设计异常，它们对软件的可理解性和可变性产生负面影响，从而阻碍软件的发展。这些糟糕的编程和设计实践可能会在某个时候导致软件的状态，在这种状态下，进行更改不再经济，从而缩短软件的生命周期。如果系统要长期使用，就必须检测、跟踪和处理不良气味--换句话说，必须积极管理。许多关于软件质量的研究都致力于Bad Smells。甚至还有软件质量认证，如软件质量指数或由德国TäVit提供的可信产品可维护性，这些认证是基于不良嗅觉的发生。这些问题包括重复代码、高耦合、低内聚力、过大以及许多其他代码方面。然而，坏气味的真正影响还没有得到充分的研究，也没有经验证明。因此，目前还不完全清楚，不良气味是否、如何以及何种类型的不良气味会对维修造成负面影响。除此之外，人们对坏气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，而忽略了演化数据。我们提出的项目旨在全面捕获软件中的不良气味，重点关注它们的演化。我们在收集的关于坏气味的数据中寻找再次出现的模式和关系。我们调查演化数据是否有助于更好地定义、检测、理解和评估Bad Smell，并评估它们是否适合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以经验为基础的维修性模型，以支持长寿命系统的演变。

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.