Cross-Dataset Design Discussion Mining

Being able to identify software discussions that are primarily about design, which we call design mining, can improve documentation and maintenance of software systems. Existing design mining approaches have good classification performance using natural language processing (NLP) techniques, but the conclusion stability of these approaches is generally poor. A classifier trained on a given dataset of software projects has so far not worked well on different artifacts or different datasets. In this study, we replicate and synthesize these earlier results in a meta-analysis. We then apply recent work in transfer learning for NLP to the problem of design mining. However, for our datasets, these deep transfer learning classifiers perform no better than less complex classifiers. We conclude by discussing some reasons behind the transfer learning approach to design mining.Comment: accepted for SANER 2020, Feb, London, ON. 12 pages. Replication package: https://doi.org/10.5281/zenodo.359012

Sustainability of Free/Libre Open Source Projects: A Longitudinal Study

This paper examines the factors that influence the long-term sustainability of FLOSS projects. A model of project sustainability based on organizational ecology is developed and tested empirically. Data about activity and contribution patterns over the course of five years for 2,772 projects registered with SourceForge is analyzed. Our results suggest that the size of the project’s development base, project age and the size of niche occupied by the project are positively related to the project’s ability to attract user and/or developer resources. The ability to attract resources is an indicator of the perceived project legitimacy, which in turn is a strong predictor of the project’s future sustainability. Thus a project’s ability to attract developer and user resources is shown to play a mediating role between the demographic (size and age) and ecological (niche) characteristics of the project and its future sustainability. Our results support the applicability of tenets of organizational ecology related to the liability of smallness, the liability of newness, and population characteristics (niche size) to the FLOSS development environment. The implications of the results for future research and practice are discussed

Enhancing Software Project Outcomes: Using Machine Learning and Open Source Data to Employ Software Project Performance Determinants

Many factors can influence the ongoing management and execution of technology projects. Some of these elements are known a priori during the project planning phase. Others require real-time data gathering and analysis throughout the lifetime of a project. These real-time project data elements are often neglected, misclassified, or otherwise misinterpreted during the project execution phase resulting in increased risk of delays, quality issues, and missed business opportunities. The overarching motivation for this research endeavor is to offer reliable improvements in software technology management and delivery. The primary purpose is to discover and analyze the impact, role, and level of influence of various project related data on the ongoing management of technology projects. The study leverages open source data regarding software performance attributes. The goal is to temper the subjectivity currently used by project managers (PMs) with quantifiable measures when assessing project execution progress. Modern-day PMs who manage software development projects are charged with an arduous task. Often, they obtain their inputs from technical leads who tend to be significantly more technical. When assessing software projects, PMs perform their role subject to the limitations of their capabilities and competencies. PMs are required to contend with the stresses of the business environment, the policies, and procedures dictated by their organizations, and resource constraints. The second purpose of this research study is to propose methods by which conventional project assessment processes can be enhanced using quantitative methods that utilize real-time project execution data. Transferability of academic research to industry application is specifically addressed vis-à-vis a delivery framework to provide meaningful data to industry practitioners

Supporting Source Code Search with Context-Aware and Semantics-Driven Query Reformulation

Software bugs and failures cost trillions of dollars every year, and could even lead to deadly accidents (e.g., Therac-25 accident). During maintenance, software developers fix numerous bugs and implement hundreds of new features by making necessary changes to the existing software code. Once an issue report (e.g., bug report, change request) is assigned to a developer, she chooses a few important keywords from the report as a search query, and then attempts to find out the exact locations in the software code that need to be either repaired or enhanced. As a part of this maintenance, developers also often select ad hoc queries on the fly, and attempt to locate the reusable code from the Internet that could assist them either in bug fixing or in feature implementation. Unfortunately, even the experienced developers often fail to construct the right search queries. Even if the developers come up with a few ad hoc queries, most of them require frequent modifications which cost significant development time and efforts. Thus, construction of an appropriate query for localizing the software bugs, programming concepts or even the reusable code is a major challenge. In this thesis, we overcome this query construction challenge with six studies, and develop a novel, effective code search solution (BugDoctor) that assists the developers in localizing the software code of interest (e.g., bugs, concepts and reusable code) during software maintenance. In particular, we reformulate a given search query (1) by designing novel keyword selection algorithms (e.g., CodeRank) that outperform the traditional alternatives (e.g., TF-IDF), (2) by leveraging the bug report quality paradigm and source document structures which were previously overlooked and (3) by exploiting the crowd knowledge and word semantics derived from Stack Overflow Q&A site, which were previously untapped. Our experiment using 5000+ search queries (bug reports, change requests, and ad hoc queries) suggests that our proposed approach can improve the given queries significantly through automated query reformulations. Comparison with 10+ existing studies on bug localization, concept location and Internet-scale code search suggests that our approach can outperform the state-of-the-art approaches with a significant margin

State of Refactoring Adoption: Towards Better Understanding Developer Perception of Refactoring

Context: Refactoring is the art of improving the structural design of a software system without altering its external behavior. Today, refactoring has become a well-established and disciplined software engineering practice that has attracted a significant amount of research presuming that refactoring is primarily motivated by the need to improve system structures. However, recent studies have shown that developers may incorporate refactoring strategies in other development-related activities that go beyond improving the design especially with the emerging challenges in contemporary software engineering. Unfortunately, these studies are limited to developer interviews and a reduced set of projects. Objective: We aim at exploring how developers document their refactoring activities during the software life cycle. We call such activity Self-Affirmed Refactoring (SAR), which is an indication of the developer-related refactoring events in the commit messages. After that, we propose an approach to identify whether a commit describes developer-related refactoring events, to classify them according to the refactoring common quality improvement categories. To complement this goal, we aim to reveal insights into how reviewers develop a decision about accepting or rejecting a submitted refactoring request, what makes such review challenging, and how to the efficiency of refactoring code review. Method: Our empirically driven study follows a mixture of qualitative and quantitative methods. We text mine refactoring-related documentation, then we develop a refactoring taxonomy, and automatically classify a large set of commits containing refactoring activities, and identify, among the various quality models presented in the literature, the ones that are more in-line with the developer\u27s vision of quality optimization, when they explicitly mention that they are refactoring to improve them to obtain an enhanced understanding of the motivation behind refactoring. After that, we performed an industrial case study with professional developers at Xerox to study the motivations, documentation practices, challenges, verification, and implications of refactoring activities during code review. Result: We introduced SAR taxonomy on how developers document their refactoring strategies in commit messages and proposed a SAR model to automate the detection of refactoring. Our survey with code reviewers has revealed several difficulties related to understanding the refactoring intent and implications on the functional and non-functional aspects of the software. Conclusion: Our SAR taxonomy and model, can work in conjunction with refactoring detectors, to report any early inconsistency between refactoring types and their documentation and can serve as a solid background for various empirical investigations. In light of our findings of the industrial case study, we recommended a procedure to properly document refactoring activities, as part of our survey feedback

From Bugs to Decision Support – Leveraging Historical Issue Reports in Software Evolution

Software developers in large projects work in complex information landscapes and staying on top of all relevant software artifacts is an acknowledged challenge. As software systems often evolve over many years, a large number of issue reports is typically managed during the lifetime of a system, representing the units of work needed for its improvement, e.g., defects to fix, requested features, or missing documentation. Efficient management of incoming issue reports requires the successful navigation of the information landscape of a project. In this thesis, we address two tasks involved in issue management: Issue Assignment (IA) and Change Impact Analysis (CIA). IA is the early task of allocating an issue report to a development team, and CIA is the subsequent activity of identifying how source code changes affect the existing software artifacts. While IA is fundamental in all large software projects, CIA is particularly important to safety-critical development. Our solution approach, grounded on surveys of industry practice as well as scientific literature, is to support navigation by combining information retrieval and machine learning into Recommendation Systems for Software Engineering (RSSE). While the sheer number of incoming issue reports might challenge the overview of a human developer, our techniques instead benefit from the availability of ever-growing training data. We leverage the volume of issue reports to develop accurate decision support for software evolution. We evaluate our proposals both by deploying an RSSE in two development teams, and by simulation scenarios, i.e., we assess the correctness of the RSSEs' output when replaying the historical inflow of issue reports. In total, more than 60,000 historical issue reports are involved in our studies, originating from the evolution of five proprietary systems for two companies. Our results show that RSSEs for both IA and CIA can help developers navigate large software projects, in terms of locating development teams and software artifacts. Finally, we discuss how to support the transfer of our results to industry, focusing on addressing the context dependency of our tool support by systematically tuning parameters to a specific operational setting