Dynamic Prediction of Delays in Software Projects using Delay Patterns and Bayesian Modeling

Modern agile software projects are subject to constant change, making it essential to re-asses overall delay risk throughout the project life cycle. Existing effort estimation models are static and not able to incorporate changes occurring during project execution. In this paper, we propose a dynamic model for continuously predicting overall...

CodeFill: Multi-token Code Completion by Jointly learning from Structure and Naming Sequences

Code completion is an essential feature of IDEs, yet current auto-completers are restricted to either grammar-based or NLP-based single token completions. Both approaches have significant draw-backs: grammar-based autocompletion is restricted in dynamically-typed language environments, whereas NLP-based autocompleters struggle to understand...

Type4Py: Practical Deep Similarity Learning-Based Type Inference for Python

Dynamic languages, such as Python and Javascript, trade static typing for developer flexibility and productivity. Lack of static typing can cause run-time exceptions and is a major factor for weak IDE support. To alleviate these issues, PEP 484 introduced optional type annotations for Python. As retrofitting types to existing code-bases is...

Modeling Team Dynamics for the Characterization and Prediction of Delays in User Stories

In agile software development, proper team structures and effort estimates are crucial to ensure the on-time delivery of software projects. Delivery performance can vary due to the influence of changes in teams, resulting in team dynamics that remain largely unexplored. In this paper, we explore the effects of various aspects of teamwork on...

Questions for Data Scientists in Software Engineering: A Replication

In 2014, a Microsoft study investigated the sort of questions that data science applied to software engineering should answer. This resulted in 145 questions that developers considered relevant for data scientists to answer, thus providing a research agenda to the community. Fast forward to five years, no further studies investigated whether the...

Releasing Fast and Slow: An Exploratory Case Study at ING

The appeal of delivering new features faster has led many software projects to adopt rapid releases. However, it is not well understood what the effects of this practice are. This paper presents an exploratory case study of rapid releases at ING, a large banking company that develops software solutions in-house, to characterize rapid releases....

Effective and Efficient API Misuse Detection via Exception Propagation and Search-Based Testing

Application Programming Interfaces (APIs) typically come with (implicit) usage constraints. The violations of these constraints (API misuses) can lead to software crashes. Even though there are several tools that can detect API misuses, most of them suffer from a very high rate of false positives. We introduce Catcher, a novel API misuse...

Software Ecosystem Call Graph for Dependency Management

A popular form of software reuse is the use of open source software libraries hosted on centralized code repositories, such as Maven or npm. Developers only need to declare dependencies to external libraries, and automated tools make them available to the workspace of the project. Recent incidents, such as the Equifax data breach and the leftpad...

How to Catch ’Em All: WatchDog, a Family of IDE Plug-Ins to Assess Testing

As software engineering researchers, we are also zealous tool smiths. Building a research prototype is often a daunting task, let alone building an industry-grade family of tools supporting multiple platforms to ensure the generalizability of results. In this paper, we give advice to academic and industrial tool smiths on how to design and build...

Pricing via Functional Size - A Case Study of a Company's Portfolio of 77 Outsourced Projects

A medium-sized west-European telecom company experienced a worsening trend in performance, indicating that the organization did not learn from history, in combination with much time and energy spent on preparation and review of project proposals. In order to create more transparency in the supplier proposal pro-cess a pilot was started on...

When, How, and Why Developers (Do Not) Test in Their IDEs

The research community in Software Engineering and Software Testing in particular builds many of its contributions on a set of mutually shared expectations. Despite the fact that they form the basis of many publications as well as open-source and commercial testing applications, these common expectations and beliefs are rarely ever questioned....

Unveiling exception handling bug hazards in Android based on GitHub and Google code issues

This paper reports on a study mining the exception stack traces included in 159,048 issues reported on Android projects hosted in GitHub (482 projects) and Google Code (157 projects). The goal of this study is to investigate whether stack trace information can reveal bug hazards related to exception handling code that may lead to a decrease...

How (Much) Do Developers Test?

What do we know about software testing in the real world? It seems we know from Fred Brooks’ seminal work “The Mythical Man-Month” that 50% of project effort is spent on testing. However, due to the enormous advances in software engineering in the past 40 years, the question stands: Is this observation still true? In fact, was it ever true? The...

An Exploratory Study of the Pull-based Software Development Model

The advent of distributed version control systems has led to the development of a new paradigm for distributed software development; instead of pushing changes to a central repository, developers pull them from other repositories and merge them locally. Various code hosting sites, notably Github, have tapped on the opportunity to facilitate pull...