Looking Over the Research Literature on Software Engineering from 2016 to 2018

This paper carries out a bibliometric analysis to detect (i) what is the most influential research on software engineering at the moment, (ii) where is being published that relevant research, (iii) what are the most commonly researched topics, (iv) and where is being undertaken that research (i.e., in which countries and institutions). For that, 6,365 software engineering articles, published from 2016 to 2018 on a variety of conferences and journals, are examined.This work has been funded by the Spanish Ministry of Science, Innovation, and Universities under Project DPI2016-77677-P, the Community of Madrid under Grant RoboCity2030-DIH-CM P2018/NMT-4331, and grant TIN2016-75850-R from the FEDER funds

Do Bugs Propagate? An Empirical Analysis of Temporal Correlations Among Software Bugs

The occurrences of bugs are not isolated events, rather they may interact, affect each other, and trigger other latent bugs. Identifying and understanding bug correlations could help developers localize bug origins, predict potential bugs, and design better architectures of software artifacts to prevent bug affection. Many studies in the defect prediction and fault localization literature implied the dependence and interactions between multiple bugs, but few of them explicitly investigate the correlations of bugs across time steps and how bugs affect each other. In this paper, we perform social network analysis on the temporal correlations between bugs across time steps on software artifact ties, i.e., software graphs. Adopted from the correlation analysis methodology in social networks, we construct software graphs of three artifact ties such as function calls and type hierarchy and then perform longitudinal logistic regressions of time-lag bug correlations on these graphs. Our experiments on four open-source projects suggest that bugs can propagate as observed on certain artifact tie graphs. Based on our findings, we propose a hybrid artifact tie graph, a synthesis of a few well-known software graphs, that exhibits a higher degree of bug propagation. Our findings shed light on research for better bug prediction and localization models and help developers to perform maintenance actions to prevent consequential bugs

An Investigation of the Effects of Merge Conflicts on Collaborative Software Development

Merge conflicts have long plagued software development. With larger and more dispersed teams comes greater risk of developers working on the same code at the same time. While merge conflicts are known to be painful, their exact impact on software is still largely unknown. Are merge conflicts an isolated problem, or are they linked to bigger issues in the code base? This thesis tries to answer this question. The first 2 parts of this thesis look at the correlation between code smells, bugs, and merge conflicts. Our results show that code that is involved in merge conflicts most frequently is more likely to have bugs, or exhibit code smells. To better understand where tool support is lacking, in the 3rd part we investigated how developers resolve merge conflicts. We used a sensemaking approach to analyze qualitative data collected in situ, where participants were working on production software. We found different patterns developers use when resolving merge conflicts, and different areas where they struggled. Our results show that merge conflicts have a real impact on software quality and also show potential avenues for tool improvements. Finally, we propose a way to predict the difficulty of a merge conflict. This can allow developers to plan ahead and set aside time and resources to handle the merge conflict resolution

Investigating the Effects of Design Quality in OSS Projects

Software systems are becoming an essential part of the lives of both individuals and organizations, and as a consequence, these systems are getting bigger and more complex. Because of this, the tasks of maintaining the quality in these complex software systems are becoming increasingly difficult. Furthermore, these systems are subject to constant pressure to add modifications, implementing new features, or regular bug fixing. Due to an often-strict release schedule, developers may not get the chance to design and implement ideal solutions. This can lead to decay in software design and the growth of technical debt, which negatively impacts the overall quality of the software. Design quality plays a vital role in maintaining the quality of software systems. In the past, several studies have shown the impact of design quality on different quality attributes such as bug density, productivity, and maintenance. Researchers also proposed several tools and techniques to improve design quality. Despite efforts by the research community in past years, there are still gaps in our understanding of how does design quality evolves in OSS projects, how projects manage design quality and how does design quality impacts the long-term health of the projects. This thesis proposes a number of large-scale empirical investigations aiming to understand the gaps. In the first part of this thesis, we investigate the evolution of design quality in OSS projects. Researchers used different metrics to measure the project’s design quality. In this thesis, we investigated two different design quality metrics. The first one is code smells, which are the result of poor design or implementation choices. And the second one is entropy, provides an understanding of design quality in terms of flexibility during the enhancement of its functionality. Our results show that design issues build up and design quality (measured by code smells and entropy) degrades over time. As the project grows older and larger, design issues are fixed less and, as a consequence, build up. In the second part, we investigate how OSS projects manage design quality. As design discussion is the primary mechanism for OSS projects to debate, make and document design decisions, we investigated how developers discuss design in the community, how they evolve along with design quality, and what effect they have on the design quality. Our results show that: I) Regardless of different communication channels used for discussion, 89:51% of all design discussions occur in the project mailing list, II) the average number of design discussions decrease; design quality degrade, as the project evolves, and III) the correlation between design discussions and design quality is small. In the third part, we investigate how does design quality impacts day to day activity (such as code merging) as well as long term health of the projects. We find that code with design issues (code smells) are more likely to be involved in merge conflicts. Our results also show that both code smells and entropy have an impact on the long-term health of the project. In this thesis, we use bug-proneness of the codebase as the measurement of the project’s long-term health

A Developer Centered Bug Prediction Model

Several techniques have been proposed to accurately predict software defects. These techniques generally exploit characteristics of the code artefacts (e.g., size, complexity, etc.) and/or of the process adopted during their development and maintenance (e.g., the number of developers working on a component) to spot out components likely containing bugs. While these bug prediction models achieve good levels of accuracy, they mostly ignore the major role played by human-related factors in the introduction of bugs. Previous studies have demonstrated that focused developers are less prone to introduce defects than non-focused developers. According to this observation, software components changed by focused developers should also be less error prone than components changed by less focused developers. We capture this observation by measuring the scattering of changes performed by developers working on a component and use this information to build a bug prediction model. Such a model has been evaluated on 26 systems and compared with four competitive techniques. The achieved results show the superiority of our model, and its high complementarity with respect to predictors commonly used in the literature. Based on this result, we also show the results of a 'hybrid' prediction model combining our predictors with the existing ones

A Developer Centered Bug Prediction Model

<pre>Several techniques have been proposed to accurately predict software defects. These techniques generally exploit characteristics of the code artefacts (i.e., size, complexity, etc.) and/or of the process adopted in their development and maintenance (i.e., the number of developers working on a component) to spot out components likely containing bugs. While these bug prediction models achieve good levels of accuracy, they mostly ignore the major role played by human-related factors in the introduction of bugs. Previous studies have demonstrated that focused developers are less prone to introduce defects than non-focused developers. According to this observation, software components changed by focused developers should also be less error prone than components changed by less focused developers. We capture this observation by measuring the scattering of changes performed by developers working on a component and use this information to build a bug prediction model. Such a model has been evaluated on 26 systems and compared with four competitive techniques. The achieved results show the superiority of our model, and its high complementarity with respect to predictors commonly used in the literature. Based on this result, we also show the results of a ``hybrid'' prediction model combining our predictors with the state-of-the-art ones.</pre