Exploring the Use of Labels to Categorize Issues in Open-Source Software Projects

International audienceReporting bugs, asking for new features and in general giving any kind of feedback is a common way to contribute to an Open-Source Software (OSS) project. This feedback is generally reported in the form of new issues for the project, managed by the so-called issue-trackers. One of the features provided by most issue-trackers is the possibility to define a set of labels/tags to classify the issues and, at least in theory, facilitate their management. Nevertheless, there is little empirical evidence to confirm that taking the time to categorize new issues has indeed a beneficial impact on the project evolution. In this paper we analyze a population of more than three million of GitHub projects and give some insights on how labels are used in them. Our preliminary results reveal that, even if the label mechanism is scarcely used, using labels favors the resolution of issues. Our analysis also suggests that not all projects use labels in the same way (e.g., for some labels are only a way to prioritize the project while others use them to signal their temporal evolution as they move along in the development workflow). Further research is needed to precisely characterize these label "families" and learn more the ideal application scenarios for each of them

Exploring the Use of Labels to Categorize Issues in Open-Source Software Projects

Abstract-Reporting bugs, asking for new features and in general giving any kind of feedback is a common way to contribute to an Open-Source Software (OSS) project. This feedback is generally reported in the form of new issues for the project, managed by the so-called issue-trackers. One of the features provided by most issue-trackers is the possibility to define a set of labels/tags to classify the issues and, at least in theory, facilitate their management. Nevertheless, there is little empirical evidence to confirm that taking the time to categorize new issues has indeed a beneficial impact on the project evolution. In this paper we analyze a population of more than three million of GitHub projects and give some insights on how labels are used in them. Our preliminary results reveal that, even if the label mechanism is scarcely used, using labels favors the resolution of issues. Our analysis also suggests that not all projects use labels in the same way (e.g., for some labels are only a way to prioritize the project while others use them to signal their temporal evolution as they move along in the development workflow). Further research is needed to precisely characterize these label "families" and learn more the ideal application scenarios for each of them

We Don't Need Another Hero? The Impact of "Heroes" on Software Development

A software project has "Hero Developers" when 80% of contributions are delivered by 20% of the developers. Are such heroes a good idea? Are too many heroes bad for software quality? Is it better to have more/less heroes for different kinds of projects? To answer these questions, we studied 661 open source projects from Public open source software (OSS) Github and 171 projects from an Enterprise Github. We find that hero projects are very common. In fact, as projects grow in size, nearly all project become hero projects. These findings motivated us to look more closely at the effects of heroes on software development. Analysis shows that the frequency to close issues and bugs are not significantly affected by the presence of project type (Public or Enterprise). Similarly, the time needed to resolve an issue/bug/enhancement is not affected by heroes or project type. This is a surprising result since, before looking at the data, we expected that increasing heroes on a project will slow down howfast that project reacts to change. However, we do find a statistically significant association between heroes, project types, and enhancement resolution rates. Heroes do not affect enhancement resolution rates in Public projects. However, in Enterprise projects, the more heroes increase the rate at which project complete enhancements. In summary, our empirical results call for a revision of a long-held truism in software engineering. Software heroes are far more common and valuable than suggested by the literature, particularly for medium to large Enterprise developments. Organizations should reflect on better ways to find and retain more of these heroesComment: 8 pages + 1 references, Accepted to International conference on Software Engineering - Software Engineering in Practice, 201

Automatic Detection of Public Development Projects in Large Open Source Ecosystems: An Exploratory Study on GitHub

Hosting over 10 million of software projects, GitHub is one of the most important data sources to study behavior of developers and software projects. However, with the increase of the size of open source datasets, the potential threats to mining these datasets have also grown. As the dataset grows, it becomes gradually unrealistic for human to confirm quality of all samples. Some studies have investigated this problem and provided solutions to avoid threats in sample selection, but some of these solutions (e.g., finding development projects) require human intervention. When the amount of data to be processed increases, these semi-automatic solutions become less useful since the effort in need for human intervention is far beyond affordable. To solve this problem, we investigated the GHTorrent dataset and proposed a method to detect public development projects. The results show that our method can effectively improve the sample selection process in two ways: (1) We provide a simple model to automatically select samples (with 0.827 precision and 0.947 recall); (2) We also offer a complex model to help researchers carefully screen samples (with 63.2% less effort than manually confirming all samples, and can achieve 0.926 precision and 0.959 recall).Comment: Accepted by the SEKE2018 Conferenc

Analysis and Detection of Information Types of Open Source Software Issue Discussions

Most modern Issue Tracking Systems (ITSs) for open source software (OSS) projects allow users to add comments to issues. Over time, these comments accumulate into discussion threads embedded with rich information about the software project, which can potentially satisfy the diverse needs of OSS stakeholders. However, discovering and retrieving relevant information from the discussion threads is a challenging task, especially when the discussions are lengthy and the number of issues in ITSs are vast. In this paper, we address this challenge by identifying the information types presented in OSS issue discussions. Through qualitative content analysis of 15 complex issue threads across three projects hosted on GitHub, we uncovered 16 information types and created a labeled corpus containing 4656 sentences. Our investigation of supervised, automated classification techniques indicated that, when prior knowledge about the issue is available, Random Forest can effectively detect most sentence types using conversational features such as the sentence length and its position. When classifying sentences from new issues, Logistic Regression can yield satisfactory performance using textual features for certain information types, while falling short on others. Our work represents a nontrivial first step towards tools and techniques for identifying and obtaining the rich information recorded in the ITSs to support various software engineering activities and to satisfy the diverse needs of OSS stakeholders.Comment: 41st ACM/IEEE International Conference on Software Engineering (ICSE2019

Aspect of Code Cloning Towards Software Bug and Imminent Maintenance: A Perspective on Open-source and Industrial Mobile Applications

As a part of the digital era of microtechnology, mobile application (app) development is evolving with lightning speed to enrich our lives and bring new challenges and risks. In particular, software bugs and failures cost trillions of dollars every year, including fatalities such as a software bug in a self-driving car that resulted in a pedestrian fatality in March 2018 and the recent Boeing-737 Max tragedies that resulted in hundreds of deaths. Software clones (duplicated fragments of code) are also found to be one of the crucial factors for having bugs or failures in software systems. There have been many significant studies on software clones and their relationships to software bugs for desktop-based applications. Unfortunately, while mobile apps have become an integral part of today’s era, there is a marked lack of such studies for mobile apps. In order to explore this important aspect, in this thesis, first, we studied the characteristics of software bugs in the context of mobile apps, which might not be prevalent for desktop-based apps such as energy-related (battery drain while using apps) and compatibility-related (different behaviors of same app in different devices) bugs/issues. Using Support Vector Machine (SVM), we classified about 3K mobile app bug reports of different open-source development sites into four categories: crash, energy, functionality and security bug. We then manually examined a subset of those bugs and found that over 50% of the bug-fixing code-changes occurred in clone code. There have been a number of studies with desktop-based software systems that clearly show the harmful impacts of code clones and their relationships to software bugs. Given that there is a marked lack of such studies for mobile apps, in our second study, we examined 11 open-source and industrial mobile apps written in two different languages (Java and Swift) and noticed that clone code is more bug-prone than non-clone code and that industrial mobile apps have a higher code clone ratio than open-source mobile apps. Furthermore, we correlated our study outcomes with those of existing desktop based studies and surveyed 23 mobile app developers to validate our findings. Along with validating our findings from the survey, we noticed that around 95% of the developers usually copy/paste (code cloning) code fragments from the popular Crowd-sourcing platform, Stack Overflow (SO) to their projects and that over 75% of such developers experience bugs after such activities (the code cloning from SO). Existing studies with desktop-based systems also showed that while SO is one of the most popular online platforms for code reuse (and code cloning), SO code fragments are usually toxic in terms of software maintenance perspective. Thus, in the third study of this thesis, we studied the consequences of code cloning from SO in different open source and industrial mobile apps. We observed that closed-source industrial apps even reused more SO code fragments than open-source mobile apps and that SO code fragments were more change-prone (such as bug) than non-SO code fragments. We also experienced that SO code fragments were related to more bugs in industrial projects than open-source ones. Our studies show how we could efficiently and effectively manage clone related software bugs for mobile apps by utilizing the positive sides of code cloning while overcoming (or at least minimizing) the negative consequences of clone fragments