Test smells are sub-optimal design choices in the implementation of test code. As reported by recent studies, their presence might not only negatively affect the comprehension of test suites but can also lead to test cases being less effective in finding bugs in production code. Although significant steps toward understanding test smells, there is still a notable absence of studies assessing their association with software quality.In this paper, we investigate the relationship between the presence of test smells and the change-and defect-proneness of test code, as well as the defect-proneness of the tested production code. To this aim, we collect data on 221 releases of ten software systems and we analyze more than a million test cases to investigate the association of six test smells and their co-occurrence with software quality. Key results of our study include:(i) tests with smells are more change-and defect-prone, (ii) 'Indirect Testing', 'Eager Test', and 'Assertion Roulette' are the most significant smells for change-proneness and, (iii) production code is more defect-prone when tested by smelly tests.
Software repositories contain historical and valuable information about the overall development of software systems. Mining software repositories (MSR) is nowadays considered one of the most interesting growing fields within software engineering. MSR focuses on extracting and analyzing data available in software repositories to uncover interesting, useful, and actionable information about the system. Even though MSR plays an important role in software engineering research, few tools have been created and made public to support developers in extracting information from Git repository. In this paper, we present Pydriller, a Python Framework that eases the process of mining Git. We compare our tool against the state-of-the-art Python Framework GitPython, demonstrating that Pydriller can achieve the same results with, on average, 50% less LOC and significantly lower complexity.
When writing automated unit tests, developers often deal with software artifacts that have several dependencies. In these cases, one has the possibility of either instantiating the dependencies or using mock objects to simulate the dependencies' expected behavior. Even though recent quantitative studies showed that mock objects are widely used in OSS projects, scientific knowledge is still lacking on how and why practitioners use mocks. Such a knowledge is fundamental to guide further research on this widespread practice and inform the design of tools and processes to improve it.The objective of this paper is to increase our understanding of which test dependencies developers (do not) mock and why, as well as what challenges developers face with this practice. To this aim, we create MOCKEXTRACTOR, a tool to mine the usage of mock objects in testing code and employ it to collect data from three OSS projects and one industrial system. Sampling from this data, we manually analyze how more than 2,000 test dependencies are treated. Subsequently, we discuss our findings with developers from these systems, identifying practices, rationales, and challenges. These results are supported by a structured survey with more than 100 professionals. The study reveals that the usage of mocks is highly dependent on the responsibility and the architectural concern of the class. Developers report to frequently mock dependencies that make testing difficult and prefer to not mock classes that encapsulate domain concepts/rules of the system. Among the key challenges, developers report that maintaining the behavior of the mock compatible with the behavior of original class is hard and that mocking increases the coupling between the test and the production code.
When testing software artifacts that have several dependencies, one has the possibility of either instantiating these dependencies or using mock objects to simulate the dependencies' expected behavior. Even though recent quantitative studies showed that mock objects are widely used both in open source and proprietary projects, scientific knowledge is still lacking on how and why practitioners use mocks. An empirical understanding of the situations where developers have (and have not) been applying mocks, as well as the impact of such decisions in terms of coupling and software evolution can be used to help practitioners adapt and improve their future usage. To this aim, we study the usage of mock objects in three OSS projects and one industrial system. More specifically, we manually analyze more than 2,000 mock usages. We then discuss our findings with developers from these systems, and identify practices, rationales, and challenges. These results are supported by a structured survey with more than 100 professionals. Finally, we manually analyze how the usage of mock objects in test code evolve over time as well as the impact of their usage on the coupling between test and production code. Our study reveals that the usage of mocks is highly dependent on the responsibility and the architectural concern of the class. Developers report to frequently mock dependencies that make testing difficult (e.g., infrastructure-related dependencies) and to not mock classes that encapsulate domain concepts/rules of the system. Among the key challenges, developers report that maintaining the behavior of the mock compatible with the behavior of original class is hard and that mocking increases the coupling between the test and the production code. Their perceptions are confirmed by our data, as we observed that mocks mostly exist since the very first version of the test class, and that they tend to stay there for its whole lifetime, and that changes in production code often force the test code to also change.
Contemporary code review is a widespread practice used by software engineers to maintain high software quality and share project knowledge. However, conducting proper code review takes time and developers often have limited time for review. In this paper, we aim at investigating the information that reviewers need to conduct a proper code review, to better understand this process and how research and tool support can make developers become more effective and efficient reviewers. Previous work has provided evidence that a successful code review process is one in which reviewers and authors actively participate and collaborate. In these cases, the threads of discussions that are saved by code review tools are a precious source of information that can be later exploited for research and practice. In this paper, we focus on this source of information as a way to gather reliable data on the aforementioned reviewers' needs. We manually analyze 900 code review comments from three large open-source projects and organize them in categories by means of a card sort. Our results highlight the presence of seven high-level information needs, such as knowing the uses of methods and variables declared/modified in the code under review. Based on these results we suggest ways in which future code review tools can better support collaboration and the reviewing task. Preprint [
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