The Co-Evolution of Test Maintenance and Code Maintenance through the lens of Fine-Grained Semantic Changes

Automatic testing is a widely adopted technique for improving software quality. Software developers add, remove and update test methods and test classes as part of the software development process as well as during the evolution phase, following the initial release. In this work we conduct a large scale study of 61 popular open source projects and report the relationships we have established between test maintenance, production code maintenance, and semantic changes (e.g, statement added, method removed, etc.). performed in developers' commits. We build predictive models, and show that the number of tests in a software project can be well predicted by employing code maintenance profiles (i.e., how many commits were performed in each of the maintenance activities: corrective, perfective, adaptive). Our findings also reveal that more often than not, developers perform code fixes without performing complementary test maintenance in the same commit (e.g., update an existing test or add a new one). When developers do perform test maintenance, it is likely to be affected by the semantic changes they perform as part of their commit. Our work is based on studying 61 popular open source projects, comprised of over 240,000 commits consisting of over 16,000,000 semantic change type instances, performed by over 4,000 software engineers.Comment: postprint, ICSME 201

Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations

Estimating development effort in free/open source software projects by mining software repositories: A case study of OpenStack

Because of the distributed and collaborative nature of free/open source software (FOSS) projects, the development effort invested in a project is usually unknown, even after the software has been released. However, this information is becoming of major interest, especially-but not only-because of the growth in the number of companies for which FOSS has become relevant for their business strategy. In this paper we present a novel approach to estimate effort by considering data from source code management repositories. We apply our model to the OpenStack project, a FOSS project with more than 1,000 authors, in which several tens of companies cooperate. Based on data from its repositories and together with the input from a survey answered by more than 100 developers, we show that the model offers a simple, but sound way of obtaining software development estimations with bounded margins of error.Gregorio Robles, Carlos Cervig on and Jes us M. Gonz alez-Barahona, project SobreSale (TIN2011-28110). and The work of Daniel Izquierdo has been funded in part by the Torres Quevedo program (PTQ-12-05577

An interview study about the use of logs in embedded software engineering

Context: Execution logs capture the run-time behavior of software systems. To assist developers in their maintenance tasks, many studies have proposed tools to analyze execution information from logs. However, it is as yet unknown how industry developers use logs in embedded software engineering. Objective: In this study, we aim to understand how developers use logs in an embedded software engineering context. Specifically, we would like to gain insights into the type of logs developers analyze, the purposes for which developers analyze logs, the information developers need from logs and their expectation on tool support. Method: In order to achieve the aim, we conducted these interview studies. First, we interviewed 25 software developers from ASML, which is a leading company in developing lithography machines. This exploratory case study provides the preliminary findings. Next, we validated and refined our findings by conducting a replication study. We involved 14 interviewees from four companies who have different software engineering roles in their daily work. Results: As the result of our first study, we compile a preliminary taxonomy which consists of four types of logs used by developers in practice, 18 purposes of using logs, 13 types of information developers search in logs, 13 challenges faced by developers in log analysis and three suggestions for tool support provided by developers. This taxonomy is refined in the replication study with three additional purposes, one additional information need, four additional challenges and three additional suggestions of tool support. In addition, with these two studies, we observed that text-based editors and self-made scripts are commonly used when it comes to tooling in log analysis practice. As indicated by the interviewees, the development of automatic analysis tools is hindered by the quality of the logs, which further suggests several challenges in log instrumentation and management. Conclusions: Based on our study, we provide suggestions for practitioners on logging practices. We provide implications for tool builders on how to further improve tools based on existing techniques. Finally, we suggest some research directions and studies for researchers to further study software logging.</p

Dynamics of Innovation in an “Open Source” Collaboration Environment: Lurking, Laboring and Launching FLOSS Projects on SourceForge

A systems analysis perspective is adopted to examine the critical properties of the Free/Libre/Open Source Software (FLOSS) mode of innovation, as reflected on the SourceForge platform (SF.net). This approach re-scales March’s (1991) framework and applies it to characterize the “innovation system” of a “distributed organization” of interacting agents in a virtual collaboration environment. The innovation system of the virtual collaboration environment is an emergent property of two “coupled” processes: one involves interactions among agents searching for information to use in designing novel software products, and the other involves the mobilization of individual capabilities for application in the software development projects. Micro-dynamics of this system are studied empirically by constructing transition probability matrices representing movements of 222,835 SF.net users among 7 different activity states. Estimated probabilities are found to form first-order Markov chains describing ergodic processes. This makes it possible to computate the equilibrium distribution of agents among the states, thereby suppressing transient effects and revealing persisting patterns of project-joining and project-launching.innovation systems, collaborative development environments, industrial districts, exploration and exploitation dynamics, open source software, FLOSS, SourceForge, project-joining, project-founding, Markov chain analysis.