A survey on software coupling relations and tools

Context Coupling relations reflect the dependencies between software entities and can be used to assess the quality of a program. For this reason, a vast amount of them has been developed, together with tools to compute their related metrics. However, this makes the coupling measures suitable for a given application challenging to find. Goals The first objective of this work is to provide a classification of the different kinds of coupling relations, together with the metrics to measure them. The second consists in presenting an overview of the tools proposed until now by the software engineering academic community to extract these metrics. Method This work constitutes a systematic literature review in software engineering. To retrieve the referenced publications, publicly available scientific research databases were used. These sources were queried using keywords inherent to software coupling. We included publications from the period 2002 to 2017 and highly cited earlier publications. A snowballing technique was used to retrieve further related material. Results Four groups of coupling relations were found: structural, dynamic, semantic and logical. A fifth set of coupling relations includes approaches too recent to be considered an independent group and measures developed for specific environments. The investigation also retrieved tools that extract the metrics belonging to each coupling group. Conclusion This study shows the directions followed by the research on software coupling: e.g., developing metrics for specific environments. Concerning the metric tools, three trends have emerged in recent years: use of visualization techniques, extensibility and scalability. Finally, some coupling metrics applications were presented (e.g., code smell detection), indicating possible future research directions. Public preprint [https://doi.org/10.5281/zenodo.2002001]

DISTANCE: a framework for software measure construction.

In this paper we present a framework for software measurement that is specifically suited to satisfy the measurement needs of empirical software engineering research. The framework offers an approach to measurement that builds upon the easily imagined, detected and visualised concepts of similarity and dissimilarity between software entities. These concepts are used both to model the software attributes of interest and to define the corresponding software measures. Central to the framework is a process model that embeds constructive procedures for attribute modelling and measure construction into a goal-oriented approach to empirical software engineering studies. The underlying measurement theoretic principles of our approach ensure the construct validity of the resulting measures. The approach was tested on a popular suite of object-oriented design measures. We further show that our measure construction method compares favourably to related work.Software;

Semantic Source Code Models Using Identifier Embeddings

The emergence of online open source repositories in the recent years has led to an explosion in the volume of openly available source code, coupled with metadata that relate to a variety of software development activities. As an effect, in line with recent advances in machine learning research, software maintenance activities are switching from symbolic formal methods to data-driven methods. In this context, the rich semantics hidden in source code identifiers provide opportunities for building semantic representations of code which can assist tasks of code search and reuse. To this end, we deliver in the form of pretrained vector space models, distributed code representations for six popular programming languages, namely, Java, Python, PHP, C, C++, and C#. The models are produced using fastText, a state-of-the-art library for learning word representations. Each model is trained on data from a single programming language; the code mined for producing all models amounts to over 13.000 repositories. We indicate dissimilarities between natural language and source code, as well as variations in coding conventions in between the different programming languages we processed. We describe how these heterogeneities guided the data preprocessing decisions we took and the selection of the training parameters in the released models. Finally, we propose potential applications of the models and discuss limitations of the models.Comment: 16th International Conference on Mining Software Repositories (MSR 2019): Data Showcase Trac