Fernanda Madeiral

Motivation : Code understandability plays a crucial role in software development, as developers spend between 58% and 70% of their time reading source code. Improving code understandability can lead to enhanced productivity and save maintenance costs. Problem: Experimental studies aim to establish what makes code more or less understandable in a controlled setting, but ignore that what makes code easier to understand in the real world also depends on extraneous elements such as project culture and guidelines, and developers’ background. Not accounting for the influence of these factors may lead to results that are sound but have little external validity. Objective : This study aims to investigate how developers improve code understandability during software development through code review comments. Its basic assumption is that code reviewers are specialists in code quality within a project. Method and Results : We manually analyzed 2,401 code review comments from Java open-source projects on GitHub and find that over 42% of all comments focus on improving code understandability, demonstrating the significance of this aspect in code reviews. We further explored a subset of 385 comments related to code understandability and identified eight categories of code understandability concerns, such as incomplete or inadequate code documentation, bad identifier, and unnecessary code. Among the suggestions to improve code understandability, 83.9% were accepted and integrated into the codebase. Among these, only two (less than 1%) end up being reverted later. We also identified types of patches that improve code understandability, ranging from simple changes (e.g., removing unused code) to more context-dependent improvements (e.g., replacing method calling chain by existing API). Finally, we evaluated the ability of four well-known linters to flag the identified code understandability issues. These linters cover less than 30% of these issues, although some of them could be easily added as new rules. Implications : Our findings motivate and provide practical insight for the construction of tools to make code more understandable, e.g., understandability improvements are rarely reverted and thus can be used as reliable training data for specialized ML-based tools. This is also supported by our dataset, which can be used to train such models. Finally, our findings can also serve as a basis to develop evidence-based code style guides. Data Availability : Our data is publicly available at https://codeupcrc.github.io.

Open Science aims to foster openness and collaboration in research, leading to more significant scientific and social impact. However, practicing Open Science comes with several challenges and is currently not properly rewarded. In this paper, we share our vision for addressing those challenges through a conceptual framework that connects essential building blocks for a change in the Software Engineering community, both culturally and technically. The idea behind this framework is that Open Science is treated as a first-class requirement for better Software Engineering research, practice, recognition, and relevant social impact. There is a long road for us, as a community, to truly embrace and gain from the benefits of Open Science. Nevertheless, we shed light on the directions for promoting the necessary culture shift and empowering the Software Engineering community.

Source code diffs are used on a daily basis as part of code review, inspection, and auditing. To facilitate understanding, they are typically accompanied by explanations that describe the essence of what is changed in the program. As manually crafting high-quality explanations is a cumbersome task, researchers have proposed automatic techniques to generate code diff explanations. Existing explanation generation methods solely focus on static analysis, i.e., they do not take advantage of runtime information to explain code changes. In this paper, we propose C ollector -S ahab , a novel tool that augments code diffs with runtime difference information. C ollector -S ahab compares the program states of the original (old) and patched (new) versions of a program to find unique variable values. Then, C ollector -S ahab adds this novel runtime information to the source code diff as shown, for instance, in code reviewing systems. As an evaluation, we run C ollector -S ahab on 584 code diffs for Defects4J bugs and find it successfully augments the code diff for 95% (555/584) of them. We also perform a user study and ask eight participants to score the augmented code diffs generated by C ollector -S ahab . Per this user study, we conclude that developers find the idea of adding runtime data to code diffs promising and useful. Overall, our experiments show the effectiveness and usefulness of C ollector -S ahab in augmenting code diffs with runtime difference information. Publicly-available repository: https://github.com/ASSERT-KTH/collector-sahab.

Source code diffs are used on a daily basis as part of code review, inspection, and auditing. To facilitate understanding, they are typically accompanied by explanations that describe the essence of what is changed in the program. As manually crafting high-quality explanations is a cumbersome task, researchers have proposed automatic techniques to generate code diff explanations. Existing explanation generation methods solely focus on static analysis, i.e., they do not take advantage of runtime information to explain code changes. In this paper, we propose Collector-Sahab, a novel tool that augments code diffs with runtime difference information. Collector-Sahab compares the program states of the original (old) and patched (new) versions of a program to find unique variable values. Then, Collector-Sahab adds this novel runtime information to the source code diff as shown, for instance, in code reviewing systems. As an evaluation, we run Collector-Sahab on 584 code diffs for Defects4J bugs and find it successfully augments the code diff for 95% (555/584) of them. We also perform a user study and ask eight participants to score the augmented code diffs generated by Collector-Sahab. Per this user study, we conclude that developers find the idea of adding runtime data to code diffs promising and useful. Overall, our experiments show the effectiveness and usefulness of Collector-Sahab in augmenting code diffs with runtime difference information. Publicly-available repository: https://github.com/ASSERT-KTH/collector-sahab.

Context: Software programs can be written in different but functionally equivalent ways. Even though previous research has compared specific formatting elements to find out which alternatives affect code legibility, seeing the bigger picture of what makes code more or less legible is challenging. Goal: We aim to find which formatting elements have been investigated in empirical studies and which alternatives were found to be more legible for human subjects. Method: We conducted a systematic literature review and identified 15 papers containing human-centric studies that directly compared alternative formatting elements. We analyzed and organized these formatting elements using a card-sorting method. Results: We identified 13 formatting elements (e.g., indentation) and 33 levels of formatting elements (e.g., two-space indentation), which are about formatting styles, spacing, block delimiters, long or complex code lines, and word boundary styles. While some levels were found to be statistically better than other equivalent ones in terms of code legibility, e.g., appropriate use of indentation with blocks, others were not, e.g., formatting layout. For identifier style, we found divergent results, where one study found a significant difference in favor of camel case, while another study found a positive result in favor of snake case. Conclusion: The number of identified papers, some of which are outdated, and the many null and contradictory results emphasize the relative lack of work in this area and underline the importance of more research. There is much to be understood about how formatting elements influence code legibility before the creation of guidelines and automated aids to help developers make their code more legible.

Ensuring the consistent usage of formatting conventions is an important aspect of modern software quality assurance. To do so, the source code of a project should be checked against the formatting conventions (or rules) adopted by its development team, and then the detected violations should be repaired if any. While the former task can be automatically done by format checkers implemented in linters, there is no satisfactory solution for the latter. Manually fixing formatting convention violations is a waste of developer time and code formatters do not take into account the conventions adopted and configured by developers for the used linter. In this paper, we present Styler, a tool dedicated to fixing formatting rule violations raised by format checkers using a machine learning approach. For a given project, Styler first generates training data by injecting violations of the project-specific rules in violation-free source code files. Then, it learns fixes by feeding long short-term memory neural networks with the training data encoded into token sequences. Finally, it predicts fixes for real formatting violations with the trained models. Currently, Styler supports a single checker, Checkstyle, which is a highly configurable and popular format checker for Java. In an empirical evaluation, Styler repaired 41% of 26,791 Checkstyle violations mined from 104 GitHub projects. Moreover, we compared Styler with the IntelliJ plugin CheckStyle-IDEA and the machine-learning-based code formatters Naturalize and CodeBuff. We found out that Styler fixes violations of a diverse set of Checkstyle rules (24/25 rules), generates smaller repairs in comparison to the other systems, and predicts repairs in seconds once trained on a project. Through a manual analysis, we identified cases in which Styler does not succeed to generate correct repairs, which can guide further improvements in Styler. Finally, the results suggest that Styler can be useful to help developers repair Checkstyle formatting violations.

Previous work has shown that early resolution of issues detected by static code analyzers can prevent major costs later on. However, developers often ignore such issues for two main reasons. First, many issues should be interpreted to determine if they correspond to actual flaws in the program. Second, static analyzers often do not present the issues in a way that is actionable. To address these problems, we present Sorald: a novel system that devise metaprogramming templates to transform the abstract syntax trees of programs and suggest fixes for static analysis warnings. Thus, the burden on the developer is reduced from interpreting and fixing static issues, to inspecting and approving full fledged solutions. Sorald fixes violations of 10 rules from SonarJava, one of the most widely used static analyzers for Java. We evaluate Sorald on a dataset of 161 popular repositories on Github. Our analysis shows the effectiveness of Sorald as it fixes 65% (852/1,307) of the violations that meets the repair preconditions. Overall, our experiments show it is possible to automatically fix notable violations of the static analysis rules produced by the state-of-the-art static analyzer SonarJava.