As the energy footprint generated by software is increasing at an alarming rate, understanding how to develop energy-efficient applications has become a necessity. Previous work has introduced catalogs of coding practices, also known as energy patterns. These patterns are yet limited to Mobile or third-party libraries. In this study, we focus on the Web domain-a main source of energy consumption. First we investigated whether and how Mobile energy patterns can be ported to this domain and found that 20 patterns could be ported. Then, we interviewed six expert web developers from different companies to challenge the ported patterns. Most developers expressed concerns for antipatterns, specifically with functional antipatterns, and were able to formulate guidelines to locate these patterns in the source code. Finally, to quantify the effect of Web energy patterns on energy consumption, we set up an automated pipeline to evaluate two ported patterns: 'Dynamic Retry Delay' (DRD) and 'Open Only When Necessary' (OOWN). With this, we found no evidence that the DRD pattern consumes less energy than its antipattern, while the opposite is true for OOWN. Data and Material: https://doi.org/10.5281/zenodo.8404487.

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Bug prediction is aimed at identifying software artifacts that are more likely to be defective in the future. Most approaches defined so far target the prediction of bugs at class/file level. Nevertheless, past research has provided evidence that this granularity is too coarse-grained for its use in practice. As a consequence, researchers have started proposing defect prediction models targeting a finer granularity (particularly method-level granularity), providing promising evidence that it is possible to operate at this level. Particularly, models mixing product and process metrics provided the best results. We present a study in which we first replicate previous research on method-level bug-prediction, by using different systems and timespans. Afterwards, based on the limitations of existing research, we (1) re-evaluate method-level bug prediction models more realistically and (2) analyze whether alternative features based on textual aspects, code smells, and developer-related factors can be exploited to improve method-level bug prediction abilities. Key results of our study include that (1) the performance of the previously proposed models, tested using the same strategy but on different systems/timespans, is confirmed; but, (2) when evaluated with a more practical strategy, all the models show a dramatic drop in performance, with results close to that of a random classifier. Finally, we find that (3) the contribution of alternative features within such models is limited and unable to improve the prediction capabilities significantly. As a consequence, our replication and negative results indicate that method-level bug prediction is still an open challenge.

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In contemporary code review, the comments put by reviewers on a specific code change are immediately visible to the other reviewers involved. Could this visibility prime new reviewers' attention (due to the human's proneness to availability bias), thus biasing the code review outcome In this study, we investigate this topic by conducting a controlled experiment with 85 developers who perform a code review and a psychological experiment. With the psychological experiment, we find that 70% of participants are prone to availability bias. However, when it comes to the code review, our experiment results show that participants are primed only when the existing code review comment is about a type of bug that is not normally considered; when this comment is visible, participants are more likely to find another occurrence of this type of bug. Moreover, this priming effect does not influence reviewers' likelihood of detecting other types of bugs. Our findings suggest that the current code review practice is effective because existing review comments about bugs in code changes are not negative primers, rather positive reminders for bugs that would otherwise be overlooked during code review. Data and materials: https://doi.org/10.5281/zenodo.

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Test smells are poor design decisions implemented in test code, which can have an impact on the effectiveness and maintainability of unit tests. Even though test smell detection tools exist, how to rank the severity of the detected smells is an open research topic. In this work, we aim at investigating the severity rating for four test smells and investigate their perceived impact on test suite maintainability by the developers. To accomplish this, we first analyzed some 1,500 open-source projects to elicit severity thresholds for commonly found test smells. Then, we conducted a study with developers to evaluate our thresholds. We found that (1) current detection rules for certain test smells are considered as too strict by the developers and (2) our newly defined severity thresholds are in line with the participants' perception of how test smells have an impact on the maintainability of a test suite. Preprint [https://doi.org/10.5281/zenodo.3744281], data and material [https://doi.org/10.5281/zenodo.3611111]. @en

Background: Code review is a cognitively demanding and time-consuming process. Previous qualitative studies hinted at how decomposing change sets into multiple yet internally coherent ones would improve the reviewing process. So far, literature provided no quantitative analysis of this hypothesis.
Aims: (1) Quantitatively measure the effects of change decomposition on the outcome of code review (in terms of number of found defects, wrongly reported issues, suggested improvements, time, and understanding); (2) Qualitatively analyze how subjects approach the review and navigate the code, building knowledge and addressing existing issues, in large vs. decomposed changes.
Method: Controlled experiment using the pull-based development model involving 28 software developers among professionals and graduate students.
Results: Change decomposition leads to fewer wrongly reported issues, influences how subjects approach and conduct the review activity (by increasing context- seeking), yet impacts neither understanding the change rationale nor the number of found defects.
Conclusions: Change decomposition reduces the noise for subsequent data analyses but also significantly supports the tasks of the developers in charge of reviewing the changes. As such, commits belonging to different concepts should be separated, adopting this as a best practice in software engineering.@en

One recent, significant advance in modeling source code for machine learning algorithms has been the introduction of path-based representation - an approach consisting in representing a snippet of code as a collection of paths from its syntax tree. Such representation efficiently captures the structure of code, which, in turn, carries its semantics and other information. Building the path-based representation involves parsing the code and extracting the paths from its syntax tree; these steps build up to a substantial technical job. With no common reusable toolkit existing for this task, the burden of mining diverts the focus of researchers from the essential work and hinders newcomers in the field of machine learning on code. In this paper, we present PathMiner - an open-source library for mining path-based representations of code. PathMiner is fast, flexible, well-tested, and easily extensible to support input code in any common programming language. Preprint [https://doi.org/10.5281/zenodo.2595271]; released tool [https://doi.org/10.5281/zenodo.2595257].

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Selecting reviewers for code changes is a critical step for an efficient code review process. Recent studies propose automated reviewer recommendation algorithms to support developers in this task. However, the evaluation of recommendation algorithms, when done apart from their target systems and users (i.e., code review tools and change authors), leaves out important aspects: perception of recommendations, influence of recommendations on human choices, and their effect on user experience. This study is the first to evaluate a reviewer recommender in vivo. We compare historical reviewers and recommendations for over 21,000 code reviews performed with a deployed recommender in a company environment and set out to measure the influence of recommendations on users' choices, along with other performance metrics. Having found no evidence of influence, we turn to the users of the recommender. Through interviews and a survey we find that, though perceived as relevant, reviewer recommendations rarely provide additional value for the respondents. We confirm this finding with a larger study at another company. The confirmation of this finding brings up a case for more user-centric approaches to designing and evaluating the recommenders. Finally, we investigate information needs of developers during reviewer selection and discuss promising directions for the next generation of reviewer recommendation tools. Preprint: https://doi.org/10.5281/zenodo.1404814.

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Healthcare mobile apps are becoming a reality for users interested in keeping their daily activities under control. In the last years, several researchers have investigated the effect of healthcare mobile apps on the life of their users as well as the positive/negative impact they have on the quality of life. Nonetheless, it remains still unclear how users approach and interact with the developers of those apps. Understanding whether healthcare mobile app users request different features with respect to other applications is important to estimate the alignment between the development process of healthcare apps and the requests of their users. In this study, we perform an empirical analysis aimed at (i) classifying the user reviews of healthcare open-source apps and (ii) analyzing the sentiment with which users write down user reviews of those apps.
In doing so, we define a manual process that enables the creation of an extended taxonomy of healthcare users’ requests. The results of our study show that users of healthcare apps are more likely to request new features and support for other hardware than users of different types of apps. Moreover, they tend to be less critical of the defects of the application and better support developers when debugging.@en

Application Programming Interfaces (API) provide reusable functionality to aid developers in the development process. The features provided by these APIs might change over time as the API evolves. To allow API consumers to peacefully transition from older obsolete features to new features, API producers make use of the deprecation mechanism that allows them to indicate to the consumer that a feature should no longer be used. The Java language designers noticed that no one was taking these deprecation warnings seriously and continued using outdated features. Due to this, they decided to change the implementation of this feature in Java 9. We question as to what extent this issue exists and whether the Java language designers have a case. We start by identifying the various ways in which an API consumer can react to deprecation. Following this we benchmark the frequency of the reaction patterns by creating a dataset consisting of data mined from 50 API consumers totalling 297,254 GitHub based projects and 1,322,612,567 type-checked method invocations. We see that predominantly consumers do not react to deprecation and we try to explain this behavior by surveying API consumers and by analyzing if the API’s deprecation policy has an impact on the consumers’ decision to react.

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Code comments are a key software component containing information about the underlying implementation. Several studies have shown that code comments enhance the readability of the code. Nevertheless, not all the comments have the same goal and target audience. In this paper, we investigate how 14 diverse Java open and closed source software projects use code comments, with the aim of understanding their purpose. Through our analysis, we produce a taxonomy of source code comments; subsequently, we investigate how often each category occur by manually classifying more than 40,000 lines of code comments from the aforementioned projects. In addition, we investigate how to automatically classify code comments at line level into our taxonomy using machine learning; initial results are promising and suggest that an accurate classification is within reach, even when training the machine learner on projects different than the target one.@en

Defect prediction models focus on identifying defect-prone code elements, for example to allow practitioners to allocate testing resources on specific subsystems and to provide assistance during code reviews. While the research community has been highly active in proposing metrics and methods to predict defects on long-term periods (i.e.,at release time), a recent trend is represented by the so-called short-term defect prediction (i.e.,at commit-level). Indeed, this strategy represents an effective alternative in terms of effort required to inspect files likely affected by defects. Nevertheless, the granularity considered by such models might be still too coarse. Indeed, existing commit-level models highlight an entire commit as defective even in cases where only specific files actually contain defects. In this paper, we first investigate to what extent commits are partially defective; then, we propose a novel fine-grained just-in-time defect prediction model to predict the specific files, contained in a commit, that are defective. Finally, we evaluate our model in terms of (i) performance and (ii) the extent to which it decreases the effort required to diagnose a defect. Our study highlights that: (1) defective commits are frequently composed of a mixture of defective and non-defective files, (2) our fine-grained model can accurately predict defective files with an AUC-ROC up to 82% and (3) our model would allow practitioners to save inspection efforts with respect to standard just-in-time techniques.

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Test-Driven Code Review (TDR) is a code review practice in which a reviewer inspects a patch by examining the changed test code before the changed production code. Although this practice has been mentioned positively by practitioners in informal literature and interviews, there is no systematic knowledge of its effects, prevalence, problems, and advantages.

In this paper, we aim at empirically understanding whether this practice has an effect on code review effectiveness and how developers' perceive TDR. We conduct (i) a controlled experiment with 93 developers that perform more than 150 reviews, and (ii) 9 semi-structured interviews and a survey with 103 respondents to gather information on how TDR is perceived. 
Key results from the experiment show that developers adopting TDR find the same proportion of defects in production code, but more in test code, at the expenses of fewer maintainability issues in production code. Furthermore, we found that most developers prefer to review production code as they deem it more critical and tests should follow from it. Moreover, general poor test code quality and no tool support hinder the adoption of TDR. 
Public preprint: https://doi.org/10.5281/zenodo.2551217, data and materials: https://doi.org/10.5281/zenodo.2553139.@en

In this study, we investigate why API producers deprecate features. Previous work has shown us that knowing the rationale behind deprecation of an API aids a consumer in deciding to react, thus hinting at a diversity of deprecation reasons. We manually analyze the Javadoc of 374 deprecated methods pertaining four mainstream Java APIs to see whether the reason behind deprecation is mentioned. We find that understanding the rationale from just the Javadoc is insufficient; hence we add other data sources such as the source code, issue tracker data and commit history. We observe 12 reasons that trigger API producers to deprecate a feature. We evaluate an automated approach to classify these motivations.@en

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 [https://doi.org/10.5281/zenodo.1405894]. Data and
Materials [https://doi.org/10.5281/zenodo.1405902].@en

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.@en

Modern distributed version control systems, such as Git, offer support for branching — the possibility to develop parts of software outside the master trunk. Consideration of the repository structure in Mining Software Repository (MSR) studies requires a thorough approach to mining, but there is no well-documented, widespread methodology regarding the handling of merge commits and branches. Moreover, there is still a lack of knowledge of the extent to which considering branches during MSR studies impacts the results of the studies. In this study, we set out to evaluate the importance of proper handling of branches when calculating file modification histories. We analyze over 1,400 Git repositories of four open source ecosystems and compute modification histories for over two million files, using two different algorithms. One algorithm only follows the first parent of each commit when traversing the repository, the other returns the full modification history of a file across all branches. We show that the two algorithms consistently deliver different results, but the scale of the difference varies across projects and ecosystems. Further, we evaluate the importance of accurate mining of file histories by comparing the performance of common techniques that rely on file modification history — reviewer recommendation, change recommendation, and defect prediction — for two algorithms of file history retrieval. We find that considering full file histories leads to an increase in the techniques’ performance that is rather modest.@en

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.

URL: https://github.com/ishepard/pydriller

Materials: https://doi.org/10.5281/zenodo.1327363

Pre-print: https://doi.org/10.5281/zenodo.1327411@en

Recent research has provided evidence that, in the industrial context, developing video games diverges from developing software systems in other domains, such as office suites and system utilities. In this paper, we consider video game development in the open source system (OSS) context. Specifically, we investigate how developers contribute to video games vs. non-games by working on different kinds of artifacts, how they handle malfunctions, and how they perceive the development process of their projects. To this purpose, we conducted a mixed, qualitative and quantitative study on a broad suite of 60 OSS projects. Our results confirm the existence of significant differences between game and non-game development, in terms of how project resources are organized and in the diversity of developers’ specializations. Moreover, game developers responding to our survey perceive more difficulties than other developers when reusing code as well as performing automated testing, and they lack a clear overview of their system’s requirements. @en

Automated testing is considered an essential process for ensuring software quality. However, writing and maintaining high-quality test code is challenging and frequently considered of secondary importance. For production code, many open source and industrial software projects employ code review, a well-established software quality practice, but the question remains whether and how code review is also used for ensuring the quality of test code. The aim of this research is to answer this question and to increase our understanding of what developers think and do when it comes to reviewing test code. We conducted both quantitative and qualitative methods to analyze more than 300,000 code reviews, and interviewed 12 developers about how they review test files. This work resulted in an overview of current code reviewing practices, a set of identified obstacles limiting the review of test code, and a set of issues that developers would like to see improved in code review tools. The study reveals that reviewing test files is very different from reviewing production files, and that the navigation within the review itself is one of the main issues developers currently face. Based on our findings, we propose a series of recommendations and suggestions for the design of tools and future research.@en

Bug prediction is aimed at supporting developers in the identification of code artifacts more likely to be defective. Researchers have proposed prediction models to identify bug prone methods and provided promising evidence that it is possible to operate at this level of granularity. Particularly, models based on a mixture of product and process metrics, used as independent variables, led to the best results.
In this study, we first replicate previous research on method- level bug prediction on different systems/timespans. Afterwards, we reflect on the evaluation strategy and propose a more realistic one. Key results of our study show that the performance of the method-level bug prediction model is similar to what previously reported also for different systems/timespans, when evaluated with the same strategy. However—when evaluated with a more realistic strategy—all the models show a dramatic drop in performance exhibiting results close to that of a random classifier. Our replication and negative results indicate that method-level bug prediction is still an open challenge.@en