All posts

Integrated Development Environments (IDEs) have become the de facto standard vehicle to develop software systems. The user interface (UI) of an IDE offers a staggering amount of facilities to manipulate source code, such as inspectors, debuggers, recommenders, alternative viewers, etc. It is unclear how developers use the UI of an IDE and whether such UIs actually give appropriate support to the developers. We present a visual approach to understand and characterize development sessions from the UI perspective. The tool supporting our approach mines and processes the finest-grained UI-level events making up development sessions and presents them visually. We have collected, visualized, and analyzed hundreds of development sessions and report on our findings.

Most development activities, like program understanding, source code navigation and editing, are supported by Integrated Development Environments (IDEs). They provide different tools and user interfaces (UI) to interact with the source code, such as browsers, debuggers, and inspectors. It is uncertain how and when programmers use different UI elements of an IDE and to what extent they appropriately support development. Previously we developed DFLOW, a tool that seamlessly records and processes interaction data. Our long-term goal is to assess to what extent the UIs of IDEs support the workflow of developers and whether they can be improved. As a first step we present our approach to analyze development sessions in the form of visual storytelling. We illustrate our initial catalogue of visualizations through two development stories.

Stack Overflow is a popular questions and answers (Q&A) website among software developers. It counts more than two millions of users who actively contribute by asking and answering thousands of questions daily. Identifying and reviewing low quality posts preserves the quality of site's contents and it is crucial to maintain a good user experience. In Stack Overflow the identification of poor quality posts is performed by selected users manually. The system also uses an automated identification system based on textual features. Low quality posts automatically enter a review queue maintained by experienced users. We present an approach to improve the automated system in use at Stack Overflow. It analyzes both the content of a post (e.g., simple textual features and complex readability metrics) and community-related aspects (e.g., popularity of a user in the community). Our approach reduces the size of the review queue effectively and removes misclassified good quality posts.

Marcello Romanelli · Master in Software Design

Developers spend a significant portion of their time understanding and learning the correct usage of the APIs of libraries they want to integrate in their projects. However, learning how to effectively use APIs is complex and time consuming. Code recommendation systems play a crucial role facilitating developers in this task by providing to them relevant examples while they code. This paper proposes a novel approach to code recommendation in which code examples are automatically obtained by mining and manipulating unit tests. In this paper we discuss the theoretical and practical implications that underpin this idea. The discussion leads to a series of fascinating research challenges that we organized in a research agenda.

One of the fundamental principles of open-source projects is that they foster collaboration among developers, disregarding their geographical location or personal background. When it comes to software repositories collaboration is a rather ephemeral phenomenon which lacks a clear definition, and it must therefore be mined and modeled. This throws up the question whether what is mined actually maps to reality. In this paper we investigate collaboration by modeling it using a number of diverse approaches that we then compare to a ground truth obtained by surveying a substantial set of developers of the Pharo open-source community. Our findings indicate that the notion of collaboration must be revisited, as it is undermined by a number of factors that are often tackled in imprecise ways or not taken into account at all.

In the recent years, several research efforts have been devoted to developing approaches to synthesize specifications of software behavior. Most of the proposed approaches addressed the inference of finite-state abstractions. The synthesized abstractions have been integrated in different validation scenarios, such as testing. While finite-state models can be effectively used as models of a software component’s behavior for certain specific purposes, they can hardly be used as full-fledged specifications. Because of their very limited expressive power, they cannot represent some of the component behaviors and may lead to synthesizing too coarse abstractions. In this paper, we survey a set of approaches that instead infer infinite-state abstractions, which can be used to express richer sets of behaviors of a software component in a black-box manner. For such approaches, we also discuss the few existing applications to software validation. In particular, we discuss the limitations and identify how, in principle, they can be used in different validation scenarios and how this opens new research directions.

Shifting the focus of software evolution research to the people-centric 'evolutionary essentials' that stakeholders need in their current working context.