Raimund Moser

A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction

In this paper we present a comparative analysis of the predictive power of two different sets of metrics for defect prediction. We choose one set of product related and one set of process related software metrics and use them for classifying Java files of the Eclipse project as defective respective defect-free. Classification models are built using three common machine learners: logistic regression, naive Bayes, and decision trees. To allow different costs for prediction errors we perform cost-sensitive classification, which proves to be very successful: >75% percentage of correctly classified files, a recall of >80%, and a false positive rate <30%. Results indicate that for the Eclipse data, process metrics are more efficient defect predictors than code metrics.

Does refactoring improve reusability

The improvement of the software development process through the development and utilization of high quality and reusable software components has been advocated for a long time. Agile Methods promote some interesting practices, in particular the practice of refactoring, which are supposed to improve understandability and maintainability of source code. In this research we analyze if refactoring promotes ad-hoc reuse of object-oriented classes by improving internal quality metrics. We conduct a case study in a close-to industrial, agile environment in order to analyze the impact of refactoring on internal quality metrics of source code. Our findings sustain the hypothesis that refactoring enhances quality and reusability of – otherwise hard to reuse – classes in an agile development environment. Given such promising results, additional experimentation is required to validate and generalize the results of this work.

Effort Prediction in Iterative Software Development Processes -- Incremental Versus Global Prediction Models

Estimation of development effort without imposing overhead on the project and the development team is of paramount importance for any software company. This study proposes a new effort estimation methodology aimed at agile and iterative development environments not suitable for description by traditional prediction methods. We propose a detailed development methodology, discuss a number of architectures of such models (including a wealth of augmented regression models and neural networks) and include a thorough case study of Extreme Programming (XP) in two semi-industrial projects. The results of this research evidence that in the XP environment under study the proposed incremental model outperforms traditional estimation techniques most notably in early phases of development. Moreover, when dealing with new projects, the incremental model can be developed from scratch without resorting itself to historic data.

A Case Study on the Impact of Refactoring on Quality and Productivity in an Agile Team

Refactoring is a hot and controversial issue. Supporters claim that it helps increasing the quality of the code, making it easier to understand, modify and maintain. Moreover, there are also claims that refactoring yields higher development productivity --- however, there is only limited empirical evidence of such assumption. A case study has been conducted to assess the impact of refactoring in a close-to industrial environment. Results indicate that refactoring not only increases aspects of software quality, but also improves productivity. Our findings are applicable to small teams working in similar, highly volatile domains (ours is application development for mobile devices). However, additional research is needed to ensure that this is indeed true and to generalize it to other contexts.

Predicting Development Effort from User Stories

In this paper, we propose a method for predicting development effort based on user stories. Such approach is well suited for Agile software projects where requirements are developed along with the project and only sketched in a rough manner. We apply the proposed method to two industrial Agile software projects of very different size and structure. We show that such effort estimation works reasonably well if user stories are written in a structured way.

Analysis of the reliability of a subset of change metrics for defect prediction

In this paper, we describe an experiment, which analyzes the relative importance and stability of change metrics for predicting defects for 3 releases of the Eclipse project. The results indicate that out of 18 change metrics 3 metrics contain most information about software defects. Moreover, those 3 metrics remain stable across 3 releases of the Eclipse project. A comparative analysis with the full model shows that the prediction accuracy is not too much affected by using a subset of 3 metrics and the recall even improves.

A Model to Identify Refactoring Effort during Maintenance by Mining Source Code Repositories

The use of refactoring as a way to continuously improve the design and quality of software and prevent its aging is mostly limited to Agile Methodologies and to a lower amount to software reengineering. In these communities refactoring is supposed to improve in the long-term the structure of existing code in order to make it easier to modify and maintain. To sustain such claims and analyze the impact of refactoring on maintenance we need to know how much refactoring developers do. In few cases such information is directly available for example from CVS log messages. In this study we propose a model on how to mine software repositories in order to obtain information of refactoring effort throughout the evolution of a software system. Moreover, we have developed a prototype that implements our model and validate our approach with two small case studies.

Visualizing software evolution with lagrein

Lagrein is a tool that allows exploring how a software system has been developed. It supports visualization of multiple metrics, it links requirements to code expected to implement them and cou-ples code with the effort spent in producing it. Moreover, it shows the systems evolution using animation and timing diagrams.

Does XP deliver quality and maintainable code

Extreme Programming aims at delivering working software for less money and still of high quality. It is well known that software maintainability is one of the most important concerns and cost factors of the software industry. The question of this research is whether Extreme Programming intrinsically delivers easily maintainable code or not. We propose a model on how to evaluate the evolution of source code quality and in particular maintainability in an Extreme Programming environment and evaluate it with a small case study. The results obtained from the case study seem to sustain the hypothesis that Extreme Programming enhances quality and in particular maintainability of a software product. Given such promising results, additional experimentation is required to validate and generalize the results of this work.

Empirical analysis on the correlation between GCC compiler warnings and revision numbers of source files in five industrial software projects

This article discusses whether using warnings generated by the GNU C++ compiler can be used effectively to identify source code files that are likely to be error prone. We analyze five industrial projects written in C++ and belonging to the telecommunication domain. We find a significant positive correlation between the number of compiler warnings and the number of source files changes. We use such correlation to conclude that compiler warnings may be used as an indicator for the presence of software defects in source code. The result of this research is useful for finding defect-prone modules in newer projects, which lack change history.