Continuous Integration at DR

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1 Continuous Integration at DR Continuous Integration and its impact on reliability and productivity in agile teams Jonas Swiatek, , Advanced Software Engineering, ITU-SASU-F2012 Diplomuddannelsen, IT Universitetet, forår 2012 Advisor: Yvonne Dittrich Page 1 of 8

2 Abstract With the Advent of Agile Methodologies in software engineering and the ever increasing focus on productivity of teams employing these, the need for systematic automated procedures such as Continuous Integration [5] and Test Driven Development has grown ever larger. This paper seeks to evaluate the implementation of Continuous Integration at DR (Danish Broadcasting Corporation) and discover how this has impacted productivity and code quality. Introduction DR is an organisation that is first and foremost a public service institution, with its main focus on Television - and is by no means a software development organisation. The software development teams are small (3-6 people), and there are on average 6 teams formed and working independently of each other at any given time. Because of this the adaptation of SCRUM has been an easy process on the managerial level. It has however given rise to a set of technical challenges. The teams collaborate with each other in a manner that is comparable to that of open source projects [3] where all source code is available in and can be collaborated upon using a web portal that gives full read access to all source code and a set of collaboration tools (such as comments and issue tracking). All collaboration and code reviews are done in an ad hoc fashion upon request or if an incident deems it necessary (such as a continually failing product). A short time to market is essential to the work being done. The news department (mainly journalists) might need a solution within as little as 30 minutes on some occasions, which leaves little time to rigorous regression and functionality testing. Being able to deploy fast and repeatedly is a crucial requirement that often outweighs the requirement to produce flawless software. Prior to the introduction of agile methodologies at DR, a centralized CMS (content management system) was used to host by far the most features of the organization's web activities. Changes to this CMS had to go through a complex review and deployment process. After anno agile, this was changed. Individual teams would develop specialized, smaller web applications, and deploy and maintain these separately to maintain velocity. This has resulted in over 100 individual web applications being developed over the course of four years, which has given rise to its very own set of challenges. While partitioning the website as a whole into this many small applications with very few interdependencies (a SOA architecture is used to share data where this is needed), a whole new set of challenges has surfaced - namely that individual teams (and even individual developers within a team) use their own preferred way to build and deploy web sites. Documentation of deployment procedures usually range in the area of non-existent and Page 2 of 8

3 poor, and very few projects provide what can be classified as good quality deployment documentation. One solution to this has been to introduce a highly standardized and automated Continuous Integration process, which automatically discovers and configures new, changed and deleted projects for automatic build, test and deployment without the developer(s) intervention. The general consensus among developers in the organisation, is that continuous integration has made deployments both easier (requiring less time), and more reliable (fewer defects when going into production). About continuous integration Continuous Integration is the practise of developers integrating their code changes into the main-line branch as often as possible [4]. This is a popular and recommended approach in many agile methodologies [5]. One premise of Continuous Integration is to unveil enough bugs, early enough, to make it worth its cost [4]. While the concept of Continuous Integration is documented in existing literature, there is a shortage of academic papers that approaches the concept in practise. The main paper available on the subject is written by Martin Fowler [2]. The following section of this paper will document how Continuous Integration is implemented at DR on a technical level. About DR s Continuous Integration Platform The main deliverable in DR s web department is a Microsoft ASP.NET MVC Web Application, which is deployed (copied) onto production servers using a proprietary deployment system dubbed Appman which main purpose is to copy files onto servers and perform the needed configuration of the destination web server. The below diagram shows how code is created at a developers computer, and the steps code goes through before it ends up on a production server. Page 3 of 8

4 One very important consequence of this system, is that there is a very high level of traceability - meaning it is very easy to see exactly who changed code when, and when code was deployed to production and what code changed since last an application was deployed. Continuous Integration is triggered automatically when a developer checks in code in the central source repository. This ensures that a developers is given feedback within as little as a minute after doing a commit. During Integration the code base goes through a set of steps including, but not limited to: 1. The code is checked out of the central source code repository 2. The code is compiled 3. All unit tests are run 4. The code is validated against a set of rules (such as HTML validation and similar) If any of the above steps fail, the integration fails as a whole and the developer is notified IM and/or directly. It is important to note that going through the continuous integration server is the only way to get code into production. This is to ensure full traceability. Definitions Continuous Integration is not a fully concrete concept, other than it implies that developers are continually integrating their work into the collective product [3]. Within the context of this paper, Continuous Integration should be considered a fully automated system, that without developer intervention, monitors a source control management system and compiles, tests and deploys code according to a very strict convention. Projects in this document a project refers to a contained code base for a specific project (e.g. the sports section of dr.dk) and all artefacts associated with it. All projects are web applications that are deployed to web servers. Research Question The research focus of this project is to evaluate Continuous Integration as a tool, and how it has impacted productivity and reliability of code being developed at DR. There are two things that are interesting to know about DR s Continuous Integration platform: What effect has Continuous Integration had on productivity? Has deployments become more reliable with Continuous Integration? In short: I want to shed light on productivity and reliability changes since the switch to continuous integration. Productivity should be understood as focusing developer time on developing software and trivialising any concern related to integration and/or deployment of code. Page 4 of 8

5 Reliability should be understood as stability of the code being deployed into a production environment. A deployment of poor quality is one containing bugs or other faults that require either a redeployment (pending a bugfix) or a rollback. Research Methods The research question aims to shed light on two factors: quality and productivity, both of which can be, to some extent, quantitatively measured. However supplementing the quantitative data with qualitative research in an attempt to triangulate a conclusion will improve the empirical validity of the research [1]. I propose to use three source of data to base this research on: 1) quantitative data based on data mining, 2) quantitative data based on a survey answered by the developers and 3) qualitative data based on interviews with developers. The two sources of data mining are the Continuous Integration Server it self, and the Appman deployment system. The Continuous Integration Server contains a full history of all builds ever performed and any build that has failed will be a case of the system pointing out a bug or configuration error to the developer. Appman is built on a relational database (Microsoft SQL Server), and has a history table that contains several years of historical data on deployments. This data can be mined to show when deployments have had to be redone, which indicates faulty code might have been put into production. The 2nd source of quantitative data is surveys answered by the developers. The purpose of these is to obtain data on the developers perceived impact of Continuous Integration of their work. The 3rd and last source of data is interviews conducted with the development staff to elicit qualitative data to try and prove or disprove any tendencies revealed by the two quantitative data sources. Data mining methods Page 5 of 8

6 When mining data from both systems it is important to maintain the focus on two factors: number of failed deployments for the appman system, and the number of failed builds for the continuous integration server. A failed build on the Continuous Integration Server is a straightforward metric: a build has either failed or succeeded, and the data is readily available in its database. A failed deployment in Appman isn t clearly defined, and to a certain degree must be guessed upon since all we know is when deployments has been performed and who performed the deployment. When a deployment is failed, the developer usually has two options available to him/her: implement a fix as fast as possible and redeploy the application, or 2) roll back to functioning a version of the application. Which of the options the developer chooses often depends on whether data migration (e.g. the database schema had to be updated) was part of the deployment. Data migrations are often complicated to roll back, and in such cases it is often opted to simply implement a fix as fast as possible. Knowing this, we can assume the following about the deployment log: 1. Any deployment prior to a rollback command can be considered defective. 2. Multiple deploys in rapid succession are indicators of defective deployments. Concerning the 2nd point, a reasonable time frame must be established of when a deployment was out right defective, and when long enough has passed that the following deployment might just be a minor adjustment to the application. A certain degree of experimentation with a variable time frame must be done, and the deployment times compared to the source control comments to establish a suitable value. Interpreting the data A strong indication that Continuous Integration has reduced the number of failed deployments would be if the drop of failed deployments after the CI server was put into use, correlates with the number of failed builds in the same period of time. This assumption should of course be made knowing that the development teams might have improved their discipline with regards to testing the code locally before committing it. Survey research Using surveys it is possible to get quantitative data on how developers perceive to spend their time. Focusing on questions relating to how much is spent getting code into production versus writing code that provides functionality requested by the customer, it is possible to gain insight into continuous integrations effect on this. Page 6 of 8

7 Some teams might have recorded time spent on integration and deployment in their backlogs - interviewing scrum masters specifically about this could yield useful information on this. Focus of the survey The focus of the survey should be put on whether or not the developers feel that integration and deployment has been trivialised, and not on the validity of CI on its own. Within DR it is already established that CI is a technique embraced by the developers - which is of little relevance to this research; it is only relevant if it has affected productivity and/or reliability. It is important to take care with the wording of this survey, as to avoid leading questions. Questions that focus on the developers rating how complex and time consuming integration and deployment is should be used, rather than having them rate their own efficiency. Interviews with development staff The quantitative data can be followed up upon with interviews of the developers which will provide qualitative data that can prove or disprove any conclusions derived from the results of the survey [1]. The interviews should, again, be focused on the developers perception of how their time is spent, and how confident they are when they start a production build. It should be considered if presenting the interviewees with the results of the quantitative research during the interview, to directly ask them if they agree with its indications. Presenting the results might affect how people respond. It could be considered presenting the results towards the end of the interview to make sure the interviewee gets a chance to voice their opinion before and after. Since we re looking mainly to prove or disprove any tendencies uncovered in the quantitative research, using a semi-structured interview form where developers are asked about their perception of Continuous Integration will likely yield answers that are useful for further study. Research reflection Because the research uses a variable metric to determine if a deployment has failed or not (during data mining of the Appman-system), great care should be taken to make sure that a good value has been chosen. This can be a time consuming task and require a lot of manual cross referencing with year old commit comments and code reviews in the source control system. A great degree of knowledge about programming would be required for anyone undertaking this task, and it might be impossible without involving the developers in the organisation - something that might not be possible because of the man hours this would require The questions of the survey need to be constructed in such a way as to not neither leading, nor too abstract to yield a trustworthy set of results. Page 7 of 8

8 Conclusion This paper has described the Continuous Integration system used by DR, and reflected upon research methods which could be used to shed light on how this has affected the quality of code and productivity of the developers. Three sources of data has been pointed out, namely data mining, survey research and interviews with development staff. Triangulation can be used to attempt to strengthen the empirical validity of any conclusion reached when analysing the results gathered from the three sources of data. References 1. Easterbrook, S., Singer, J., Storey, M., Damian, D. Selecting Empirical Methods for Software Engineering Research (Chapter 11). 2. Fowler, M. Continuous Integration, continuousintegration.html 3. Holck, J., Jørgensen, N. Continuous Integration and Quality Assurance. A Case Study of two Open Source Projects. Australasian Journal of Information Systems (Special Issue 2003/2004) 4. Huo, M., Verner J., Zhu, L., Ali Babar, M. Software Quality and Agile Methods, National ICT Australia Ltd. and University of New South Wales, Australia 5. Nerur, S., Mahapatra, R., Mangalaraj, G. Challenges of Migrating to Agile Methodologies. Communications of the ACM (May 2005/Vol. 48. No. 5) Page 8 of 8

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