Data analytics in SE

Image by Werner Weisser from Pixabay

A few years ago, data analytics and big data were super popular in software engineering. In fact, they were a bit too popular, as many authors quoted big data because they had a diagram in the paper.

Fast forward to today and the situation is a bit different. We are more mature in using data in software development. We know that Big data is about the 5 Vs and that we can reason about it. We also know what providing the diagrams is not the same as using them to direct software development.

I found this paper when looking for literature for our new work on communication in software metrics teams. My colleagues study the communication and found that there can be several sources of confusion. Now, this paper is NOT about the confusion, but about prevalence of data analytics in software engineering. The working definition of Big Data Analytics is as follows in the paper: “Big data analytics is the process of using analysis algorithms running on powerful supporting platforms to uncover potentials concealed in big data, such as hidden patterns or unknown correlations”.

The paper poses three main research questions about the studies conducted in Big Data Analytics, about the approaches used and when they are used. I’m mostly interested in the second – which approaches are used. There, the authors pose three sub-questions:

RQ2.1: What types of analytics have been used in the ASD domain?
RQ2.2: What sources of data have been used?
RQ2.3: What methods, models, or techniques have been utilized in the studies?

In particular, the second one is the most interesting one – sources of data. There, the authors found that there are plenty. The entire table (Table 7 in the paper) is actually too large to quote, but let me just quote one of the categories: Source code and data model:

  • Source code
  • Ruby programs & Ruby on Rails
  • Java programs
  • Function calls
  • Code metrics
  • Development repository
  • Test case
  • Code quality
  • Application data schema

I recommend this as a good reading into the current state-of-the-art in data analytics in software engineering. I think we’ve matured a lot in the last decade as a community and that brings a lot of benefit. Our software development gets better and thus our software gets better.

From the abstract: In total, 88 primary studies were selected and analyzed. Our results show that BDA is employed throughout the whole ASD lifecycle. The results reveal that data-driven software development is focused on the following areas: code repository analytics, defects/bug fixing, testing, project management analytics, and application usage analytics.

Building interactive dashboards

Image by StockSnap from Pixabay 

Review of the book Interactive Visual Data Analysis: by Christian Tominski, Heidrun Schumann

Designing a good dashboard is an art. We need to answer questions like – who will use the dashboard? for what? when? and how will the interaction happen. Our team has studied dashboards and developed a model for choosing which one is the most suitable one ( ).

However, we never studied what is important when constructing a dashboard, but the authors of this book did.

I’m super happy to have stumbled upon this book, because it has shown me how to think when constructing dashboards. It shows which elements are important and how to create actionable visual analytics, not how to use JavaScript to create a diagram.

I liked chapter 3 and 4 the most. They show how to move from simple visualizations to interactions and how to work with parameters of the visualizations. However, one needs to read chapter 2 as well to get some inspiration about which diagrams to use and how to use the visual attentive attributes like color, size or shape – both separately and in combination.

To sum up, if you want to construct a good dashboard, please read the book and I can promise you, it will not dissapoint you.