Exploranative Code Quality Documents

Good code quality is a prerequisite for efficiently developing maintainable software. In this paper, we present a novel approach to generate exploranative (explanatory and exploratory) data-driven documents that report code quality in an interactive, exploratory environment. We employ a template-based natural language generation method to create textual explanations about the code quality, dependent on data from software metrics. The interactive document is enriched by different kinds of visualization, including parallel coordinates plots and scatterplots for data exploration and graphics embedded into text. We devise an interaction model that allows users to explore code quality with consistent linking between text and visualizations; through integrated explanatory text, users are taught background knowledge about code quality aspects. Our approach to interactive documents was developed in a design study process that included software engineering and visual analytics experts. Although the solution is specific to the software engineering scenario, we discuss how the concept could generalize to multivariate data and report lessons learned in a broader scope.Comment: IEEE VIS VAST 201

Evolution of statistical analysis in empirical software engineering research: Current state and steps forward

Software engineering research is evolving and papers are increasingly based on empirical data from a multitude of sources, using statistical tests to determine if and to what degree empirical evidence supports their hypotheses. To investigate the practices and trends of statistical analysis in empirical software engineering (ESE), this paper presents a review of a large pool of papers from top-ranked software engineering journals. First, we manually reviewed 161 papers and in the second phase of our method, we conducted a more extensive semi-automatic classification of papers spanning the years 2001--2015 and 5,196 papers. Results from both review steps was used to: i) identify and analyze the predominant practices in ESE (e.g., using t-test or ANOVA), as well as relevant trends in usage of specific statistical methods (e.g., nonparametric tests and effect size measures) and, ii) develop a conceptual model for a statistical analysis workflow with suggestions on how to apply different statistical methods as well as guidelines to avoid pitfalls. Lastly, we confirm existing claims that current ESE practices lack a standard to report practical significance of results. We illustrate how practical significance can be discussed in terms of both the statistical analysis and in the practitioner's context.Comment: journal submission, 34 pages, 8 figure

Community Smells -- The Sources of Social Debt: A Systematic Literature Review

Context: Social debt describes the accumulation of unforeseen project costs (or potential costs) from sub-optimal software development processes. Community smells are sociotechnical anti-patterns and one source of social debt that impact software teams, development processes, outcomes, and organizations. Objective: To provide an overview of community smells based on published literature, and describe future research. Method: We conducted a systematic literature review (SLR) to identify properties, understand origins and evolution, and describe the emergence of community smells. This SLR explains the impact of community smells on teamwork and team performance. Results: We include 25 studies. Social debt describes the impacts of poor socio-technical decisions on work environments, people, software products, and society. For each of the 30 identified community smells, we provide a description, management approaches, organizational strategies, and mitigation effectiveness. We identify five groups of management approaches: organizational strategies, frameworks, models, tools, and guidelines. We describe 11 properties of community smells. We develop the Community Smell Stages Framework to concisely describe the origin and evolution of community smells. We describe the causes and effects for each community smell. We identify and describe 8 types of causes and 11 types of effects for community smells. Finally, we provide 8 Sankey diagrams that offer insights into threats the community smells pose to teamwork factors and team performance. Conclusion: Community smells explain the influence work conditions have on software developers. The literature is scarce and focuses on a small number of community smells. Thus, community smells still need more research. This review organizes the state of the art about community smells and provides motivation for future research along with educational material.Comment: Accepted for publication in Information and Software Technolog

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

Detecting modularity "smells" in dependencies injected with Java annotations

Dependency injection is a recent programming mechanism reducing dependencies among components by delegating them to an external entity, called a dependency injection framework. An increasingly popular approach to dependency injection implementation relies upon using Java annotations, a special form of syntactic metadata provided by the dependency injection frameworks. However, uncontrolled use of annotations may lead to potential violations of well-known modularity principles. In this paper we catalogue "bad smells", i.e., modularity-violating annotations defined by the developer or originating from the popular dependency injection frameworks. For each violation we discuss potential implications and propose means of resolving it. By detecting modularity bad smells in Java annotations our approach closes the gap between the state-of-the-art programming practice and currently available analysis techniques