What is the Connection Between Issues, Bugs, and Enhancements? (Lessons Learned from 800+ Software Projects)

Agile teams juggle multiple tasks so professionals are often assigned to multiple projects, especially in service organizations that monitor and maintain a large suite of software for a large user base. If we could predict changes in project conditions changes, then managers could better adjust the staff allocated to those projects.This paper builds such a predictor using data from 832 open source and proprietary applications. Using a time series analysis of the last 4 months of issues, we can forecast how many bug reports and enhancement requests will be generated next month. The forecasts made in this way only require a frequency count of this issue reports (and do not require an historical record of bugs found in the project). That is, this kind of predictive model is very easy to deploy within a project. We hence strongly recommend this method for forecasting future issues, enhancements, and bugs in a project.Comment: Accepted to 2018 International Conference on Software Engineering, at the software engineering in practice track. 10 pages, 10 figure

Ethical Issues in Empirical Studies of Software Engineering

The popularity of empirical methods in software engineering research is on the rise. Surveys, experiments, metrics, case studies, and field studies are examples of empirical methods used to investigate both software engineering processes and products. The increased application of empirical methods has also brought about an increase in discussions about adapting these methods to the peculiarities of software engineering. In contrast, the ethical issues raised by empirical methods have received little, if any, attention in the software engineering literature. This article is intended to introduce the ethical issues raised by empirical research to the software engineering research community, and to stimulate discussion of how best to deal with these ethical issues. Through a review of the ethical codes of several fields that commonly employ humans and artifacts as research subjects, we have identified major ethical issues relevant to empirical studies of software engineering. These issues are illustrated with real empirical studies of software engineering

Return of the Great Spaghetti Monster : Learnings from a Twelve-Year Adventure in Web Software Development

The widespread adoption of the World Wide Web has fundamentally changed the landscape of software development. Only ten years ago, very few developers would write software for the Web, let alone consider using JavaScript or other web technologies for writing any serious software applications. In this paper, we reflect upon a twelve-year adventure in web development that began with the development of the Lively Kernel system at Sun Microsystems Labs in 2006. Back then, we also published some papers that identified important challenges in web-based software development based on established software engineering principles. We will revisit our earlier findings and compare the state of the art in web development today to our earlier learnings, followed by some reflections and suggestions for the road forward.Peer reviewe

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

Cognitive dimensions usability assessment of textual and visual VHDL environments

Visual programming languages promise to make programming easier with simpler graphical methods, broadening access to computing by lessening the need for would-be users to become proficient with textual programming languages, with their somewhat arcane grammars and methods removed from the problem space of the user. However, after more than forty years of research in the field, visual methods remain in the margins of use and programming remains the bailiwick of people devoted to the endeavor. VPL designers need to understand the mechanisms of usability that pertain to complex systems like programming language environments. Effective research tools for studying usability, and sufficiently constrained, mature subjects for investigation are scarce. This study applies a usability research tool, with its origins in applied psychology, to a programming language surrogate from the hardware description language class of notations. The substitution is reasonable because of the great similarity between hardware description languages and programming languages. Considering VHDL (the VHSIC Hardware Description Language) is especially worthwhile for several reasons, but primarily because significant numbers of digital designers regularly employ both textual and visual VHDL environments to meet the same real-world design challenges. A comparative analysis of Cognitive Dimensions assessments of textual and visual VHDL environments should further understanding of the usability issues specifically related to visual methods – in many cases, the same visual methods used in visual programming languages. Furthermore, with this real-world ‘field lab’ better understood, it should be possible to design experiments to pursue the formalization of the CDs framework as a theory

A Survey on Automated Program Repair Techniques

With the rapid development and large-scale popularity of program software, modern society increasingly relies on software systems. However, the problems exposed by software have also come to the fore. Software defect has become an important factor troubling developers. In this context, Automated Program Repair (APR) techniques have emerged, aiming to automatically fix software defect problems and reduce manual debugging work. In particular, benefiting from the advances in deep learning, numerous learning-based APR techniques have emerged in recent years, which also bring new opportunities for APR research. To give researchers a quick overview of APR techniques' complete development and future opportunities, we revisit the evolution of APR techniques and discuss in depth the latest advances in APR research. In this paper, the development of APR techniques is introduced in terms of four different patch generation schemes: search-based, constraint-based, template-based, and learning-based. Moreover, we propose a uniform set of criteria to review and compare each APR tool, summarize the advantages and disadvantages of APR techniques, and discuss the current state of APR development. Furthermore, we introduce the research on the related technical areas of APR that have also provided a strong motivation to advance APR development. Finally, we analyze current challenges and future directions, especially highlighting the critical opportunities that large language models bring to APR research.Comment: This paper's earlier version was submitted to CSUR in August 202