Software developers and teams typically rely on source code and tasks management tools for their projects. They tend to depend on different platforms such as GitHub, Azure DevOps, Bitbucket, and GitLab for task-tracking, feature-tracking, and bug-tracking to develop and maintain their software repositories. Individually, developers may lose concentration when having to navigate through numerous screens consisting of various platforms to perform daily tasks. Additionally, while in meetings (non-virtual), teams are often separate from their machines and often would have to rely on pure recollection of the tasks and issues related to their work. This can delay the decision-making process and take away valuable focus hours of developers. Although there is usually one person with their laptop to guide the meeting and has access to the source code management tools, this can take a lot of time as they are not familiar with all the developers’ independent works. Therefore, a new tool needs to be introduced to help accelerate individual and team meetings’ productivity. In this paper, we continued the work on Robin, a voice-assistant built to answer questions regarding GitHub issues and source code management. Robin has the ability to answer questions in addition to completing actions on the behalf of the developer. This thesis presents Robin's abilities, architecture, and implementation while also examining its usability through a user study. Our study suggests that some people love the idea of having a conversational agent for software development. However, a lot more research and iterations must be done to fully make Robin give the user experience we imagined. In this thesis, we were able to set the foundation of this idea and the lessons that we learned.

No description available.

No description available.

As software becomes more important to society, the number, age, and complexity of systems grow. Software organizations require continuous process improvement to maintain the reliability, security, and quality of these software systems. Software organizations can utilize data from manual fault classification to meet their process improvement needs, but organizations lack the expertise or resources to implement them correctly. This dissertation addresses the need for the automation of software fault classification. Validation results show that automated fault classification, as implemented in the MiSFIT tool, can group faults of similar nature. The resulting classifications result in good agreement for common software faults with no manual effort. To evaluate the method and tool, I develop and apply an extended change taxonomy to classify the source code changes that repaired software faults from an open source project. MiSFIT clusters the faults based on the changes. I manually inspect a random sample of faults from each cluster to validate the results. The automatically classified faults are used to analyze the evolution of a software application over seven major releases. The contributions of this dissertation are an extended change taxonomy for software fault analysis, a method to cluster faults by the syntax of the repair, empirical evidence that fault distribution varies according to the purpose of the module, and the identification of project-specific trends from the analysis of the changes.

The main objective of this thesis is to identify how the software repository features influence software quality during software evolution. To do that the mining software repository area was used. This field analyzes the rich data from software repositories to extract interesting and actionable information about software systems, projects and software engineering. The ability to measure code quality and analyze the impact of software repository features on software quality allows us to better understand project history, project quality state, development processes and conduct future project analysis. Existing work in the area of software quality describes software quality analysis without a connection to the software repository features. Thus they lose important information that can be used for preventing bugs, decision-making and optimizing development processes. To conduct the analysis specific tool was developed, which cover quality measurement and repository features extraction. During the research general procedure of the software quality analysis was defined, described and applied in practice. It was found that there is no most influential repository feature and the correlation between software quality and software repository features exist, but it is too small to make a real influence.

abstract: The following is a case study composed of three workflow investigations at the open source software development (OSSD) based Apache Software Foundation (Apache). I start with an examination of the workload inequality within the Apache, particularly with regard to requirements writing. I established that the stronger a participant's experience indicators are, the more likely they are to propose a requirement that is not a defect and the more likely the requirement is eventually implemented. Requirements at Apache are divided into work tickets (tickets). In our second investigation, I reported many insights into the distribution patterns of these tickets. The participants that create the tickets often had the best track records for determining who should participate in that ticket. Tickets that were at one point volunteered for (self-assigned) had a lower incident of neglect but in some cases were also associated with severe delay. When a participant claims a ticket but postpones the work involved, these tickets exist without a solution for five to ten times as long, depending on the circumstances. I make recommendations that may reduce the incidence of tickets that are claimed but not implemented in a timely manner. After giving an in-depth explanation of how I obtained this data set through web crawlers, I describe the pattern mining platform I developed to make my data mining efforts highly scalable and repeatable. Lastly, I used process mining techniques to show that workflow patterns vary greatly within teams at Apache. I investigated a variety of process choices and how they might be influencing the outcomes of OSSD projects. I report a moderately negative association between how often a team updates the specifics of a requirement and how often requirements are completed. I also verified that the prevalence of volunteerism indicators is positively associated with work completion but what was surprising is that this correlation is stronger if I exclude the very large projects. I suggest the largest projects at Apache may benefit from some level of traditional delegation in addition to the phenomenon of volunteerism that OSSD is normally associated with. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2017

Software maintenance and the understanding of where in the source code features are implemented are two strongly coupled tasks that make up a large portion of the effort spent on developing applications. The concept of feature location investigated in this thesis can serve as a supporting factor in those tasks as it facilitates the automation of otherwise manual searches for source code artifacts. Challenges in this subject area include the aggregation and composition of a training corpus from historical codebase data for models as well as the integration and optimization of qualified topic modeling techniques. Building up on previous research, this thesis provides a comparison of two different techniques and introduces a toolkit that can be used to reproduce and extend on the results discussed. Specifically, in this thesis a changeset-based approach to feature location is pursued and applied to a large open-source Java project. The project is used to optimize and evaluate the performance of Latent Dirichlet Allocation models and Pachinko Allocation models, as well as to compare the accuracy of the two models with each other. As discussed at the end of the thesis, the results do not indicate a clear favorite between the models. Instead, the outcome of the comparison depends on the metric and viewpoint from which it is assessed.

No description available.

No description available.

This thesis explores mining the evolutionary history of a software system to support software developers and managers in their endeavors to build and maintain complex software systems. We introduce the idea of evolutionary extractors which are specialized extractors that can recover the history of software projects from software repositories, such as source control systems. The challenges faced in building C-REX, an evolutionary extractor for the C programming language, are discussed. We examine the use of source control systems in industry and the quality of the recovered C-REX data through a survey of several software practitioners. Using the data recovered by C-REX, we develop several approaches and techniques to assist developers and managers in their activities. We propose <em>Source Sticky Notes</em> to assist developers in understanding legacy software systems by attaching historical information to the dependency graph. We present the <em>Development Replay</em> approach to estimate the benefits of adopting new software maintenance tools by reenacting the development history. We propose the <em>Top Ten List</em> which assists managers in allocating testing resources to the subsystems that are most susceptible to have faults. To assist managers in improving the quality of their projects, we present a complexity metric which quantifies the complexity of the changes to the code instead of quantifying the complexity of the source code itself. All presented approaches are validated empirically using data from several large open source systems. The presented work highlights the benefits of transforming software repositories from static record keeping repositories to active repositories used by researchers to gain empirically based understanding of software development, and by software practitioners to predict, plan and understand various aspects of their project.