Özet:

Software is intangible and, therefore, difficult to understand and control. Quality problems often creep in unnoticed because the developers do not have a full view of the history and all the consequences of their changes to the system. Therefore, we observe quality decay in many software systems over time. A countermeasure would be frequent quality analyses starting early in the development, e.g. in nightly builds. Frequent quality analyses reduce the problem of quality decay because quality defects are detected soon after they were introduced, but they still allow defects to be introduced. This means that builds might fail, other developers might have already worked with that code revision and changes will result in rework. I propose to apply the detection of quality problems directly before or while they are created. For this, we need continuous and focused feedback about a software's quality to the developers. To give that feedback to the developers without overwhelming them, a main goal of this project is to understand the quality information needs of developers. We will build an information needs theory by empirical analysis and use this theory to investigate diverse quality checks integrated directly into development environments. We will build on advances in test-case generation, static analysis and repository mining to provide this feedback. Finally, we will extensively investigate the impact of the created continuous and focused feedback on the developers in controlled experiments and industrial case studies. We see the main challenges in providing the right feedback to the developer at the right time and in the right context as well as supplying the developers with the necessary explanations to understand and make use of the shown information. We will address these challenges by building focused and learning analyses which understand the context of the change and the developer. The analyses will learn manually and automatically the developer's preferences. Furthermore, we mine and include rationales from detailed quality models, web resources and slicing into the developer feedback. If we succeed, the results will increase the usage of quality analyses in practice, make the developers aware of quality problems, and reduce quality problems and the expenditure they cause. Furthermore, our project will open up possibilities for research to create and integrate new kinds of immediate feedback to the developers.

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