Do practitioners intentionally repay their own Technical Debt and why?

The impact of Technical Debt (TD) on software maintenance and evolution is of great concern, but recent evidence shows that a considerable amount of TD is fixed by the same developers who introduced it; this is termed self-fixed TD. This characteristic of TD management can potentially impact team dynamics and practices in managing TD. However, the initial evidence is based on low-level source code analysis; this casts some doubt whether practitioners repay their own debt intentionally and under what circumstances. To address this gap, we conducted an online survey on 17 well-known Java and Python open-source software communities to investigate practitioners’ intent and rationale for self-fixing technical debt. We also investigate the relationship between human-related factors (e.g., experience) and self-fixing. The results, derived from the responses of 181 participants, show that a majority addresses their own debt consciously and often. Moreover, those with a higher level of involvement (e.g., more experience in the project and number of contributions) tend to be more concerned about self-fixing TD. We also learned that the sense of responsibility is a common self-fixing driver and that decisions to fix TD are not superficial but consider balancing costs and benefits, among other factors. The findings in this paper can lead to improving TD prevention and management strategies

Understanding flaky tests: the developer’s perspective

Flaky tests are software tests that exhibit a seemingly random outcome (pass or fail) despite exercising unchanged code. In this work, we examine the perceptions of software developers about the nature, relevance, and challenges of flaky tests. We asked 21 professional developers to classify 200 flaky tests they previously fixed, in terms of the nature and the origin of the flakiness, as well as of the fixing effort. We also examined developers' fixing strategies. Subsequently, we conducted an online survey with 121 developers with a median industrial programming experience of five years. Our research shows that: The flakiness is due to several different causes, four of which have never been reported before, despite being the most costly to fix; flakiness is perceived as significant by the vast majority of developers, regardless of their team's size and project's domain, and it can have effects on resource allocation, scheduling, and the perceived reliability of the test suite; and the challenges developers report to face regard mostly the reproduction of the flaky behavior and the identification of the cause for the flakiness. Public preprint [http://arxiv.org/abs/1907.01466], data and materials [https://doi.org/10.5281/zenodo.3265785]

Automating Test Case Identification in Open Source Projects on GitHub

Software testing is one of the very important Quality Assurance (QA) components. A lot of researchers deal with the testing process in terms of tester motivation and how tests should or should not be written. However, it is not known from the recommendations how the tests are actually written in real projects. In this paper the following was investigated: (i) the denotation of the test word in different natural languages; (ii) whether the test word correlates with the presence of test cases; and (iii) what testing frameworks are mostly used. The analysis was performed on 38 GitHub open source repositories thoroughly selected from the set of 4.3M GitHub projects. We analyzed 20,340 test cases in 803 classes manually and 170k classes using an automated approach. The results show that: (i) there exists weak correlation (r = 0.655) between the word test and test cases presence in a class; (ii) the proposed algorithm using static file analysis correctly detected 95\% of test cases; (iii) 15\% of the analyzed classes used main() function whose represent regular Java programs that test the production code without using any third-party framework. The identification of such tests is very low due to implementation diversity. The results may be leveraged to more quickly identify and locate test cases in a repository, to understand practices in customized testing solutions and to mine tests to improve program comprehension in the future.Comment: 31 page