Software practitioners make technical and business decisions based on the understanding they have of their software systems. This understanding is grounded in their own experiences, but can be augmented by studying various kinds of development artifacts, including source code, bug reports, version control meta-data, test cases, usage logs, etc. Unfortunately, the information contained in these artifacts is typically not organized in the way that is immediately useful to developers’ everyday decision making needs. To handle the large volumes of data, many practitioners and researchers have turned to analytics — that is, the use of analysis, data, and systematic reasoning for making decisions. The thesis of this dissertation is that by employing software analytics to various development tasks and activities, we can provide software practitioners better insights into their processes, systems, products, and users, to help them make more informed data-driven decisions. While quantitative analytics can help project managers understand the big picture of their systems, plan for its future, and monitor trends, qualitative analytics can enable developers to perform their daily tasks and activities more quickly by helping them better manage high volumes of information. To support this thesis, we provide three different examples of employing software analytics. First, we show how analysis of real-world usage data can be used to assess user dynamic behaviour and adoption trends of a software system by revealing valuable information on how software systems are used in practice. Second, we have created a lifecycle model that synthesizes knowledge from software development artifacts, such as reported issues, source code, discussions, community contributions, etc. Lifecycle models capture the dynamic nature of how various development artifacts change over time in an annotated graphical form that can be easily understood and communicated. We demonstrate how lifecycle models can be generated and present industrial case studies where we apply these models to assess the code review process of three different projects. Third, we present a developer-centric approach to issue tracking that aims to reduce information overload and improve developers’ situational awareness. Our approach is motivated by a grounded theory study of developer interviews, which suggests that customized views of a project’s repositories that are tailored to developer-specific tasks can help developers better track their progress and understand the surrounding technical context of their working environments. We have created a model of the kinds of information elements that developers feel are essential in completing their daily tasks, and from this model we have developed a prototype tool organized around developer-specific customized dashboards. The results of these three studies show that software analytics can inform evidence-based decisions related to user adoption of a software project, code review processes, and improved developers’ awareness on their daily tasks and activities.

There is a lack in research on the perspectives of different managerial roles on data about software projects in software analytics tools, such as the perspectives of chief financial officers (CFOs), chief executive officers (CEOs) and compliance officers. Today, software analytics tools are mainly developed to address the needs of technical stakeholders such as developers, but research shows that there exist potentials of expanding this technical users’ scope of focus to also include higher level stakeholders, such as managers. The goal of this study is to explore what managers working in software development organizations consider to be useful data to have about software projects in software analytics tools, as well as examining how they want data about software projects to be presented to them in such tools. This study was done in four steps. First, a literature review was conducted. Second, a questionnaire was conducted with four CFOs, one CEO and one compliance officer working in six different Swedish software development organizations. Third, semi-structured interviews were conducted with three CFOs, one CEO and one compliance officer working in five different Swedish software development organizations. Fourth, a visual prototype simulating a software analytics tool was constructed based on the data gathered from the interviews. The result of this study shows that abstraction, limitation, and visualization of data about software projects, as well as presentation of useful data in software analytics tools that support the work tasks of managers, is helpful in addressing the perspectives and views of the target group.

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

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