Numerous tools exist for mining source code and software development process metrics. However, very few publicly available tools focus on source code comments, a crucial software artifact. This paper presents SoCCMiner (Source Code-Comments and Comment-Context Miner), a tool that offers multiple mining pipelines. It is the first readily available (plug-and-play) and customizable open-source tool for mining source code contextual information of comments at different granularities (Class comments, Method comments, Interface comments, and other granular comments). Mining comments at different source code granularities can aid researchers and practitioners working in a host of applications that focus on source code comments, such as Self-Admitted Technical Debt, Program Comprehension, and other applications. Furthermore, SoCCMiner is highly adaptable and extendable to include additional attributes and support other programming languages. This prototype supports the Java programming language.

A high imbalance exists between technical debt and non-technical debt source code comments. Such imbalance affects Self-Admitted Technical Debt (SATD) detection performance, and existing literature lacks empirical evidence on the choice of balancing technique. In this work, we evaluate the impact of multiple balancing techniques, including Data level, Classifier level, and Hybrid, for SATD detection in Within-Project and Cross-Project setup. Our results show that the Data level balancing technique SMOTE or Classifier level Ensemble approaches Random Forest or XGBoost are reasonable choices depending on whether the goal is to maximize Precision, Recall, F1, or AUC-ROC. We compared our best-performing model with the previous SATD detection benchmark (cost-sensitive Convolution Neural Network). Interestingly the top-performing XGBoost with SMOTE sampling improved the Within-project F1 score by 10% but fell short in Cross-Project set up by 9%. This supports the higher generalization capability of deep learning in Cross-Project SATD detection, yet while working within individual projects, classical machine learning algorithms can deliver better performance. We also evaluate and quantify the impact of duplicate source code comments in SATD detection performance. Finally, we employ SHAP and discuss the interpreted SATD features. We have included the replication package1and shared a web-based SATD prediction tool2with the balancing techniques in this study.

Reports of poor work well-being and fluctuating productivity in software engineering have been reported in both academic and popular sources. Understanding and predicting these issues through repository analysis might help manage software developers' well-being. Our objective is to link data from software repositories, that is commit activity, communication, expressed sentiments, and job events, with measures of well-being obtained with a daily experience sampling questionnaire. To achieve our objective, we studied a single software project team for eight months in the software industry. Additionally, we performed semi-structured interviews to explain our results. The acquired quantitative data are analyzed with generalized linear mixed-effects models with autocorrelation structure. We find that individual variance accounts for most of the $R^2$ values in models predicting developers' experienced well-being and productivity. In other words, using software repository variables to predict developers' well-being or productivity is challenging due to individual differences. Prediction models developed for each developer individually work better, with fixed effects $R^2$ value of up to 0.24. The semi-structured interviews give insights into the well-being of software developers and the benefits of chat interaction. Our study suggests that individualized prediction models are needed for well-being and productivity prediction in software development.

Data of long-lived and high profile projects is valuable for research on successful software engineering in the wild. Having a dataset with different linked software repositories of such projects, enables deeper diving investigations. This paper presents 20-MAD, a dataset linking the commit and issue data of Mozilla and Apache projects. It includes over 20 years of information about 765 projects, 3.4M commits, 2.3M issues, and 17.3M issue comments, and its compressed size is over 6 GB. The data contains all the typical information about source code commits (e.g., lines added and removed, message and commit time) and issues (status, severity, votes, and summary). The issue comments have been pre-processed for natural language processing and sentiment analysis. This includes emoticons and valence and arousal scores. Linking code repository and issue tracker information, allows studying individuals in two types of repositories and provide more accurate time zone information for issue trackers as well. To our knowledge, this the largest linked dataset in size and in project lifetime that is not based on GitHub.

Background: Using sentiment analysis to study software developers' behavior comes with challenges such as the presence of a large amount of technical discussion unlikely to express any positive or negative sentiment. However, emoticons provide information about developer sentiments that can easily be extracted from software repositories. Aim: We investigate how software developers use emoticons differently in issue trackers in order to better understand the differences between developers and determine to which extent emoticons can be used as in place of sentiment analysis. Method: We extract emoticons from 1.3M comments from Apache's issue tracker and 4.5M from Mozilla's issue tracker using regular expressions built from a list of emoticons used by SentiStrength and Wikipedia. We check for statistical differences using Mann-Whitney U tests and determine the effect size with Cliff's δ. Results: Overall Mozilla developers rely more on emoticons than Apache developers. While the overall rate of comments with emoticons is of 1% and 3% for Apache and Mozilla, some individual developers can have a rate up to 21%. Looking specifically at Mozilla developers, we find that western developers use significantly more emoticons (with medium size effect) than eastern developers. While the majority of emoticons are used to express joy, we find that Mozilla developers use emoticons more frequently to express sadness and surprise than Apache developers. Finally, we find that Apache developers use overall more emoticons during weekends than during weekdays, with the share of sad and surprised emoticons increasing during weekends. Conclusions: While emoticons are primarily used to express joy, the more occasional use of sad and surprised emoticons can potentially be utilized to detect frustration in place of sentiment analysis among developers using emoticons frequently enough.

Background: The experience sampling method studies everyday experiences of humans in natural environments. In psychology it has been used to study the relationships between work well-being and productivity. To our best knowledge, daily experience sampling has not been previously used in software engineering. Aims: Our aim is to identify links between software developers self-reported affective states and work well-being and measures obtained from software repositories. Method: We perform an experience sampling study in a software company for a period of eight months, we use logistic regression to link the well-being measures with development activities, i.e. number of commits and chat messages. Results: We find several significant relationships between questionnaire variables and software repository variables. To our surprise relationship between hurry and number of commits is negative, meaning more perceived hurry is linked with a smaller number of commits. We also find a negative relationship between social interaction and hindered work well-being. Conclusions: The negative link between commits and hurry is counter-intuitive and goes against previous lab-experiments in software engineering that show increased efficiency under time pressure. Overall, our is an initial step in using experience sampling in software engineering and validating theories on work well-being from other fields in the domain of software engineering.

Background: Unstructured and textual data is increasing rapidly and Latent Dirichlet Allocation (LDA) topic modeling is a popular data analysis methods for it. Past work suggests that instability of LDA topics may lead to systematic errors. Aim: We propose a method that relies on replicated LDA runs, clustering, and providing a stability metric for the topics. Method: We generate k LDA topics and replicate this process n times resulting in n*k topics. Then we use K-medioids to cluster the n*k topics to k clusters. The k clusters now represent the original LDA topics and we present them like normal LDA topics showing the ten most probable words. For the clusters, we try multiple stability metrics, out of which we recommend Rank-Biased Overlap, showing the stability of the topics inside the clusters. Results: We provide an initial validation where our method is used for 270,000 Mozilla Firefox commit messages with k=20 and n=20. We show how our topic stability metrics are related to the contents of the topics. Conclusions: Advances in text mining enable us to analyze large masses of text in software engineering but non-deterministic algorithms, such as LDA, may lead to unreplicable conclusions. Our approach makes LDA stability transparent and is also complementary rather than alternative to many prior works that focus on LDA parameter tuning.

Abnormal working hours can reduce work health, general well-being, and productivity, independent from a profession. To inform future approaches for automatic stress and overload detection, this paper establishes empirically collected measures of the work patterns of software engineers. To this aim, we perform the first large-scale study of software engineers' working hours by investigating the time stamps of commit activities of 86 large open source software projects, both containing hired and volunteer developers. We find that two thirds of software engineers mainly follow typical office hours, empirically established to be from 10h to 18h, and do not usually work during nights and weekends. Large variations between projects and individuals exist. Surprisingly, we found no support that project maturation would decrease abnormal working hours. In the Firefox case study, we found that hired developers work more during office hours while seniority, either in terms of number of commits or job status, did not impact working hours. We conclude that the use of working hours or timestamps of work products for stress detection requires establishing baselines at the level of individuals.

Open source development contains contributions from both hired and volunteer software developers. Identification of this status is important when we consider the transferability of research results to the closed source software industry, as they include no volunteer developers. While many studies have taken the employment status of developers into account, this information is often gathered manually due to the lack of accurate automatic methods. In this paper, we present an initial step towards predicting paid and unpaid open source development using machine learning and compare our results with automatic techniques used in prior work. By relying on code source repository meta-data from Mozilla, and manually collected employment status, we built a dataset of the most active developers, both volunteer and hired by Mozilla. We define a set of metrics based on developers' usual commit time pattern and use different classification methods (logistic regression, classification tree, and random forest). The results show that our proposed method identify paid and unpaid commits with an AUC of 0.75 using random forest, which is higher than the AUC of 0.64 obtained with the best of the previously used automatic methods.

The use of natural language processing (NLP) is gaining popularity in software engineering. In order to correctly perform NLP, we must pre-process the textual information to separate natural language from other information, such as log messages, that are often part of the communication in software engineering. We present a simple approach for classifying whether some textual input is natural language or not. Although our NLoN package relies on only 11 language features and character tri-grams, we are able to achieve an area under the ROC curve performances between 0.976-0.987 on three different data sources, with Lasso regression from Glmnet as our learner and two human raters for providing ground truth. Cross-source prediction performance is lower and has more fluctuation with top ROC performances from 0.913 to 0.980. Compared with prior work, our approach offers similar performance but is considerably more lightweight, making it easier to apply in software engineering text mining pipelines. Our source code and data are provided as an R-package for further improvements.

During the past years, overload at work leading to psychological diseases, such as burnouts, have drawn more public attention. This paper is a preliminary step toward an analysis of the work patterns and possible indicators of overload and time pressure on software developers with mining software repositories approach. We explore the working pattern of developers in the context of Mozilla Firefox, a large and long-lived open source project. To that end we investigate the impact of the move from traditional to rapid release cycle on work pattern. Moreover we compare Mozilla Firefox work pattern with another Mozilla product, Firefox OS, which has a different release cycle than Firefox. We find that both projects exhibit healthy working patterns, i.e. lower activity during the weekends and outside of office hours. Firefox experiences proportionally more activity on weekends than Firefox OS (Cohen's d = 0.94). We find that switching to rapid releases has reduced weekend work (Cohen's d = 1.43) and working during the night (Cohen's d = 0.45). This result holds even when we limit the analyzes on the hired resources, i.e. considering only individuals with Mozilla foundation email address, although, the effect sizes are smaller for weekends (Cohen's d = 0.64) and nights (Cohen's d = 0.23). Moreover, we use dissimilarity word clouds and find that work during the weekend is more technical while work during the week expresses more positive sentiment with words like "good" and "nice". Our results suggest that moving to rapid releases have positive impact on the work health and work-life-balance of software engineers. However, caution is needed as our results are based on a limited set of quantitative data from a single organization.

Emotional arousal increases activation and performance but may also lead to burnout in software development. We present the first version of a Software Engineering Arousal lexicon (SEA) that is specifically designed to address the problem of emotional arousal in the software developer ecosystem. SEA is built using a bootstrapping approach that combines word embedding model trained on issue-tracking data and manual scoring of items in the lexicon. We show that our lexicon is able to differentiate between issue priorities, which are a source of emotional activation and then act as a proxy for arousal. The best performance is obtained by combining SEA (428 words) with a previously created general purpose lexicon by Warriner et al. (13,915 words) and it achieves Cohen's d effect sizes up to 0.5.

Nearly every popular programming language comes with one or more open source software packaging ecosystem(s), containing a large collection of interdependent software packages developed in that programming language. Such packaging ecosystems are extremely useful for their respective software development community. We present an empirical analysis of how the dependency graphs of three large packaging ecosystems (npm, CRAN and RubyGems) evolve over time. We study how the existing package dependencies impact the resilience of the three ecosystems over time and to which extent these ecosystems suffer from issues related to package dependency updates. We analyse specific solutions that each ecosystem has put into place and argue that none of these solutions is perfect, motivating the need for better tools to deal with package dependency update problems.

When developing software packages in a software ecosystem, an important and well-known challenge is how to deal with dependencies to other packages. In presence of multiple package repositories, dependency management tends to become even more problematic. For the R ecosystem of statistical computing, dependency management is currently insufficient to deal with multiple package versions and inter-repository package dependencies. We explore how the use of GitHub influences the R ecosystem, both for the distribution of R packages and for inter-repository package dependency management. We also discuss how these problems could be addressed.

Users and developers of software distributions are often confronted with installation problems due to conflicting packages. A prototypical example of this are the Linux distributions such as Debian. Conflicts between packages have been studied under different points of view in the literature, in particular for the Debian operating system, but little is known about how these package conflicts evolve over time. This article presents an extensive analysis of the evolution of package incompatibilities, spanning a decade of the life of the Debian stable and testing distributions for its most popular architecture, i386. Using the technique of survival analysis, this empirical study sheds some light on the origin and evolution of package incompatibilities, and provides the basis for building indicators that may be used to improve the quality of package-based distributions.

The R development community maintains thousands of packages through its Comprehensive R Archive Network CRAN. The growth and evolution of this archive makes it more and more difficult to maintain packages and their interdependencies, and the existing tools that aim to help developers in this process no longer suffice. We propose maintaine R, a web-based dashboard that allows CRAN package developers to understand and deal with the implications and problems raised by package updates. The dashboard complements existing analysis tools by providing additional support such as the visualisation of package dependencies and reverse dependencies, package conflicts, cross-package function clones, and so on.

We present a dataset of the open source software ecosystem Gnome from a social point of view. We have collected historical data about the contributors to all Gnome projects stored on git.gnome.org, taking into account the problem of identity matching, and associating different activity types to the contributors. This type of information is very useful to complement the traditional, source-code related information one can obtain by mining and analyzing the actual source code. The dataset can be obtained at https://bitbucket.org/mgoeminne/sgl-flossmetric-dbmerge.