Mining unstructured software data

Our thesis is that the analysis of unstructured data supports software understanding and evolution analysis, and complements the data mined from structured sources. To this aim, we implemented the necessary toolset and investigated methods for exploring, exposing, and exploiting unstructured data.To validate our thesis, we focused on development email data. We found two main challenges in using it to support program comprehension and software development: The disconnection between emails and code artifacts and the noisy and mixed-language nature of email content. We tackle these challenges proposing novel approaches. First, we devise lightweight techniques for linking email data to code artifacts. We use these techniques for creating a tool to support program comprehension with email data, and to create a new set of email based metrics to improve existing defect prediction approaches. Subsequently, we devise techniques for giving a structure to the content of email and we use this structure to conduct novel software analyses to support program comprehension. In this dissertation we show that unstructured data, in the form of development emails, is a valuable addition to structured data and, if correctly mined, can be used successfully to support software engineering activities

Distributed and Collaborative Software Evolution Analysis with Churrasco

AbstractAnalyzing the evolution of large and long-lived software systems is a complex problem that requires extensive tool support due to the amount and complexity of the data that needs to be processed. In this paper, we present Churrasco, a tool to support collaborative software evolution analysis through a web interface. After describing the tool and its architecture, we provide a usage scenario of Churrasco on a large open source software system, and we present two collaboration experiments performed with, respectively, 8 and 4 participants

Visually localizing design problems with disharmony maps

Assessing the quality of software design is difficult, as “design” is expressed through guidelines and heuristics, not rigorous rules. One successful approach to assess design quality is based on de-tection strategies, which are metrics-based composed logical condi-tions, by which design fragments with specific properties are de-tected in the source code. Such detection strategies, when exe-cuted on large software systems usually return large sets of arti-facts, which potentially exhibit one or more “design disharmonies”, which are then inspected manually, a cumbersome activity. In this article we present disharmony maps, a visualization-based approach to locate such flawed software artifacts in large systems. We display the whole system using a 3D visualization technique based on a city metaphor. We enrich such visualizations with the results returned by a number of detection strategies, and thus render both the static structure and the design problems that affect a subject system. We evaluate our approach on a number of open-source Java systems and report on our findings

Change-centric improvement of team collaboration

In software development, teamwork is essential to the successful delivery of a final product. The software industry has historically built software utilizing development teams that share the workplace. Process models, tools, and methodologies have been enhanced to support the development of software in a collocated setting. However, since the dawn of the 21st century, this scenario has begun to change: an increasing number of software companies are adopting global software development to cut costs and speed up the development process. Global software development introduces several challenges for the creation of quality software, from the adaptation of current methods, tools, techniques, etc., to new challenges imposed by the distributed setting, including physical and cultural distance between teams, communication problems, and coordination breakdowns. A particular challenge for distributed teams is the maintenance of a level of collaboration naturally present in collocated teams. Collaboration in this situation naturally d r ops due to low awareness of the activity of the team. Awareness is intrinsic to a collocated team, being obtained through human interaction such as informal conversation or meetings. For a distributed team, however, geographical distance and a subsequent lack of human interaction negatively impact this awareness. This dissertation focuses on the improvement of collaboration, especially within geographically dispersed teams. Our thesis is that by modeling the evolution of a software system in terms of fine-grained changes, we can produce a detailed history that may be leveraged to help developers collaborate. To validate this claim, we first c r eate a model to accurately represent the evolution of a system as sequences of fine- grained changes. We proceed to build a tool infrastructure able to capture and store fine-grained changes for both immediate and later use. Upon this foundation, we devise and evaluate a number of applications for our work with two distinct goals: 1. To assist developers with real-time information about the activity of the team. These applications aim to improve developers’ awareness of team member activity that can impact their work. We propose visualizations to notify developers of ongoing change activity, as well as a new technique for detecting and informing developers about potential emerging conflicts. 2. To help developers satisfy their needs for information related to the evolution of the software system. These applications aim to exploit the detailed change history generated by our approach in order to help developers find answers to questions arising during their work. To this end, we present two new measurements of code expertise, and a novel approach to replaying past changes according to user-defined criteria. We evaluate the approach and applications by adopting appropriate empirical methods for each case. A total of two case studies – one controlled experiment, and one qualitative user study – are reported. The results provide evidence that applications leveraging a fine-grained change history of a software system can effectively help developers collaborate in a distributed setting

Holistic recommender systems for software engineering

The knowledge possessed by developers is often not sufficient to overcome a programming problem. Short of talking to teammates, when available, developers often gather additional knowledge from development artifacts (e.g., project documentation), as well as online resources. The web has become an essential component in the modern developer’s daily life, providing a plethora of information from sources like forums, tutorials, Q&A websites, API documentation, and even video tutorials. Recommender Systems for Software Engineering (RSSE) provide developers with assistance to navigate the information space, automatically suggest useful items, and reduce the time required to locate the needed information. Current RSSEs consider development artifacts as containers of homogeneous information in form of pure text. However, text is a means to represent heterogeneous information provided by, for example, natural language, source code, interchange formats (e.g., XML, JSON), and stack traces. Interpreting the information from a pure textual point of view misses the intrinsic heterogeneity of the artifacts, thus leading to a reductionist approach. We propose the concept of Holistic Recommender Systems for Software Engineering (H-RSSE), i.e., RSSEs that go beyond the textual interpretation of the information contained in development artifacts. Our thesis is that modeling and aggregating information in a holistic fashion enables novel and advanced analyses of development artifacts. To validate our thesis we developed a framework to extract, model and analyze information contained in development artifacts in a reusable meta- information model. We show how RSSEs benefit from a meta-information model, since it enables customized and novel analyses built on top of our framework. The information can be thus reinterpreted from an holistic point of view, preserving its multi-dimensionality, and opening the path towards the concept of holistic recommender systems for software engineering

Refining code ownership with synchronous changes

When mining software repositories, two distinct sources of information are usually explored: the history log and snapshots of the system. Results of analyses derived from these two sources are biased by the frequency with which developers commit their changes. We argue that the usage of mainstream SCM (software configuration management) systems influences the way that developers work. For example, since it is tedious to resolve conflicts due to parallel commits, developers tend to minimize conflicts by not contemporarily modifying the same file. This however defeats one of the purposes of such systems. We mine repositories created by our tool Syde, which records changes in a central repository whenever a file is compiled locally in the IDE (integrated development environment) by any developer in a multi-developer project. This new source of information can augment the accuracy of analyses and breaks new ground in terms of how such information can assist developers. We illustrate how the information we mine provides a refined notion of code ownership with respect to the one inferred by SCM system data. We demonstrate our approach on three case studies, including an industrial one. Ownership models suffer from the assumption that developers have a perfect memory. To account for their imperfect memory, we integrate into our ownership measurement a model of memory retention, to simulate the effect of memory loss over time. We evaluate the characteristics of this model for several strengths of memor

Evaluating defect prediction approaches: a benchmark and an extensive comparison

Reliably predicting software defects is one of the holy grails of software engineering. Researchers have devised and implemented a plethora of defect/bug prediction approaches varying in terms of accuracy, complexity and the input data they require. However, the absence of an established benchmark makes it hard, if not impossible, to compare approaches. We present a benchmark for defect prediction, in the form of a publicly available dataset consisting of several software systems, and provide an extensive comparison of well-known bug prediction approaches, together with novel approaches we devised. We evaluate the performance of the approaches using different performance indicators: classification of entities as defect-prone or not, ranking of the entities, with and without taking into account the effort to review an entity. We performed three sets of experiments aimed at (1) comparing the approaches across different systems, (2) testing whether the differences in performance are statistically significant, and (3) investigating the stability of approaches across different learners. Our results indicate that, while some approaches perform better than others in a statistically significant manner, external validity in defect prediction is still an open problem, as generalizing results to different contexts/learners proved to be a partially unsuccessful endeavo

Interaction-aware development environments: recording, mining, and leveraging IDE interactions to analyze and support the development flow

Nowadays, software development is largely carried out using Integrated Development Environments, or IDEs. An IDE is a collection of tools and facilities to support the most diverse software engineering activities, such as writing code, debugging, and program understanding. The fact that they are integrated enables developers to find all the tools needed for the development in the same place. Each activity is composed of many basic events, such as clicking on a menu item in the IDE, opening a new user interface to browse the source code of a method, or adding a new statement in the body of a method. While working, developers generate thousands of these interactions, that we call fine-grained IDE interaction data. We believe this data is a valuable source of information that can be leveraged to enable better analyses and to offer novel support to developers. However, this data is largely neglected by modern IDEs. In this dissertation we propose the concept of "Interaction-Aware Development Environments": IDEs that collect, mine, and leverage the interactions of developers to support and simplify their workflow. We formulate our thesis as follows: Interaction-Aware Development Environments enable novel and in- depth analyses of the behavior of software developers and set the ground to provide developers with effective and actionable support for their activities inside the IDE. For example, by monitoring how developers navigate source code, the IDE could suggest the program entities that are potentially relevant for a particular task. Our research focuses on three main directions: 1. Modeling and Persisting Interaction Data. The first step to make IDEs aware of interaction data is to overcome its ephemeral nature. To do so we have to model this new source of data and to persist it, making it available for further use. 2. Interpreting Interaction Data. One of the biggest challenges of our research is making sense of the millions of interactions generated by developers. We propose several models to interpret this data, for example, by reconstructing high-level development activities from interaction histories or measure the navigation efficiency of developers. 3. Supporting Developers with Interaction Data. Novel IDEs can use the potential of interaction data to support software development. For example, they can identify the UI components that are potentially unnecessary for the future and suggest developers to close them, reducing the visual cluttering of the IDE