Several code smells detection tools have been developed providing different results, because smells can be subjectively interpreted and hence detected in different ways. Usually the detection techniques are based on the computation of different kinds of metrics, and other aspects related to the domain of the system under analysis, its size and other design features are not taken into account. In this paper we propose an approach we are studying based on machine learning techniques. We outline some common problems faced for smells detection and we describe the different steps of our approach and the algorithms we use for the classification.

There are numerous commercial tools and research prototypes that offer support for measuring technical debt. However, different tools adopt different terms, metrics, and ways to identify and measure technical debt. These tools offer diverse features, and their popularity / community support varies significantly. Therefore, (a) practitioners face difficulties when trying to select a tool matching their needs; and (b) the concept of technical debt and its role in software development is blurred. We attempt to clarify the situation by comparing the features and popularity of technical debt measurement tools, and analyzing the existing empirical evidence on their validity. Our findings can help practitioners to find the most suitable tool for their purposes, and researchers by highlighting the current tool shortcomings.

Code smells are poor implementation choices applied by developers during software evolution that often lead to critical flaws or failure. Much in the same way, community smells reflect the presence of organizational and socio-technical issues within a software community that may lead to additional project costs. Recent empirical studies provide evidence that community smells are often---if not always---connected to circumstances such as code smells. In this paper we look deeper into this connection by conducting a mixed-methods empirical study of 117 releases from 9 open-source systems. The qualitative and quantitative sides of our mixed-methods study were run in parallel and assume a mutually-confirmative connotation. On the one hand, we survey 162 developers of the 9 considered systems to investigate whether developers perceive relationship between community smells and the code smells found in those projects. On the other hand, we perform a fine-grained analysis into the 117 releases of our dataset to measure the extent to which community smells impact code smell intensity (i.e., criticality). We then propose a code smell intensity prediction model that relies on both technical and community-related aspects. The results of both sides of our mixed-methods study lead to one conclusion: community-related factors contribute to the intensity of code smells. This conclusion supports the joint use of community and code smells detection as a mechanism for the joint management of technical and social problems around software development communities.