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  • 651.
    Ögren, Mikael
    et al.
    Linköping University, Department of Computer and Information Science, Software and Systems.
    Wikblad, Ludwig
    Linköping University, Department of Computer and Information Science, Software and Systems.
    En testprocess för webbutvecklingsprojekt med små team2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Finding a suitable approach for testing in small development teams is a challenge. Many small companies view traditional test processes and test process improvement models as too resource intensive for their needs. Minimal Test Practice Framework (MTPF) is a framework for testing which purpose is to provide a minimalistic approach to test improvement. The goal of this study was to examine how MTPF can be adapted to a small development team without incurring a time cost that the team would experience as too high. The study was performed in the department Web \& Mobile of the company Exsitec. At the department teams of 2-6 people develop web applications to business customers. During the study a testprocess was developed in close cooperation with the developers of the department with the aim of adapting it as well as possible to the needs of the department. The study was performed as action research in three phases, according to the method Cooperative Method Development, in a project with two developers. During the first phase all developers in the department were interviewed to establish an understanding of the environment for the study. During the second phase a set of possible improvements was developed together with the developers. During the third phase some of these improvements were implemented and evaluated. By focusing on unit testing central business logic in the application the developed test process improved the developers confidence in the code quality without being perceived as too resource intensive.

  • 652.
    Öhberg, Tomas
    Linköping University, Department of Computer and Information Science, Software and Systems.
    Auto-tuning Hybrid CPU-GPU Execution of Algorithmic Skeletons in SkePU2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The trend in computer architectures has for several years been heterogeneous systems consisting of a regular CPU and at least one additional, specialized processing unit, such as a GPU.The different characteristics of the processing units and the requirement of multiple tools and programming languages makes programming of such systems a challenging task. Although there exist tools for programming each processing unit, utilizing the full potential of a heterogeneous computer still requires specialized implementations involving multiple frameworks and hand-tuning of parameters.To fully exploit the performance of heterogeneous systems for a single computation, hybrid execution is needed, i.e. execution where the workload is distributed between multiple, heterogeneous processing units, working simultaneously on the computation.

    This thesis presents the implementation of a new hybrid execution backend in the algorithmic skeleton framework SkePU. The skeleton framework already gives programmers a user-friendly interface to algorithmic templates, executable on different hardware using OpenMP, CUDA and OpenCL. With this extension it is now also possible to divide the computational work of the skeletons between multiple processing units, such as between a CPU and a GPU. The results show an improvement in execution time with the hybrid execution implementation for all skeletons in SkePU. It is also shown that the new implementation results in a lower and more predictable execution time compared to a dynamic scheduling approach based on an earlier implementation of hybrid execution in SkePU.

  • 653.
    Öhlin, Petra
    Linköping University, Department of Computer and Information Science, Software and Systems.
    Prioritizing Tests with Spotify’s Test & Build Data using History-based, Modification-based & Machine Learning Approaches2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This thesis intends to determine the extent to which machine learning can be used to solve the regression test prioritization (RTP) problem. RTP is used to order tests with respect to probability of failure. This will optimize for a fast failure, which is desirable if a test suite takes a long time to run or uses a significant amount of computational resources. A common machine learning task is to predict probabilities; this makes RTP an interesting application of machine learning. A supervised learning method is investigated to train a model to predict probabilities of failure, given a test case and a code change. The features investigated are chosen based on previous research of history- based and modification-based RTP. The main motivation for looking at these research areas is that they resemble the data provided by Spotify. The result of the report shows that it is possible to improve how tests run with RTP using machine learning. Nevertheless, a much simpler history- based approach is the best performing approach. It is looking at the history of test results, the more failures recorded for the test case over time, the higher priority it gets. Less is sometimes more. 

  • 654.
    Östman, Nicklas
    et al.
    Linköping University, Department of Computer and Information Science, Software and Systems.
    Lindström, Rasmus
    Linköping University, Department of Computer and Information Science, Software and Systems.
    Vertically Scaling Agile: A Multiple-Case Study2017Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The conceptual framework of agile software development is an ever-growing movement in the software industry. However, recent studies have shown that large, less software-focused companies, where software development is primarily used for in-house IT-solutions, struggle with giving up traditional command-control type of management. This hits hard on some of the most important principles of agile software development and in many cases this phenomenon has inevitably led to large gaps between development teams and more managerial parts of the organization. This thesis has aimed to study this gap and investigate how it affects software development teams’ ability to carry out their work.

    By comparing three software teams that were internally highly similar but with varying external conditions, impact on the teams’ behaviour based on their different environments was studied. The study was carried out using a multiple-case study approach with primary data sources consisting of survey gathered data from all team members and interviews with a subset of the team members. The results gathered from this study suggest that agile development teams are extremely dependent on a well-functioning interface to business related parts of an organization. Regarding teams’ ability to make decisions and being agile in their way of working, the results primarily isolate impediments with roots in an unwillingness to adhere to and lack of understanding of agile principles.

    In this thesis, our gathered results were also correlated with a modern framework called Flow in order to confirm its relevance regarding analyzing software development teams in large-scale environments. 

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