A framework for semi-automated software evolution analysis composition

Software evolution data stored in repositories such as version control, bug and issue tracking, or mailing lists is crucial to better understand a software system and assess its quality. A myriad of analyses exploiting such data have been proposed throughout the years. However, easy and straight forward synergies between these analyses rarely exist. To tackle this problem we have investigated the concept of Software Analysis as a Service and devised SOFAS, a distributed and collaborative software evolution analysis platform. Software analyses are offered as services that can be accessed, composed into workflows, and executed over the Internet. This paper presents our framework for composing these analyses into workflows, consisting of a custom-made modeling language and a composition infrastructure for the service offerings. The framework exploits the RESTful nature of our analysis service architecture and comes with a service composer to enable semi-automated service compositions by a user. We validate our framework by showcasing two different approaches built on top of it that support different stakeholders in gaining a deeper insight into a project history and evolution. As a result, our framework has shown its applicability to deliver diverse, complex analyses across system and tool boundarie

Quality Assessment and Prediction in Software Product Lines

At the heart of product line development is the assumption that through structured reuse later products will be of a higher quality and require less time and effort to develop and test. This thesis presents empirical results from two case studies aimed at assessing the quality aspect of this claim and exploring fault prediction in the context of software product lines. The first case study examines pre-release faults and change proneness of four products in PolyFlow, a medium-sized, industrial software product line; the second case study analyzes post-release faults using pre-release data over seven releases of four products in Eclipse, a very large, open source software product line.;The goals of our research are (1) to determine the association between various software metrics, as well as their correlation with the number of faults at the component/package level; (2) to characterize the fault and change proneness of components/packages at various levels of reuse; (3) to explore the benefits of the structured reuse found in software product lines; and (4) to evaluate the effectiveness of predictive models, built on a variety of products in a software product line, to make accurate predictions of pre-release software faults (in the case of PolyFlow) and post-release software faults (in the case of Eclipse).;The research results of both studies confirm, in a software product line setting, the findings of others that faults (both pre- and post-release) are more highly correlated to change metrics than to static code metrics, and are mostly contained in a small set of components/ packages. The longitudinal aspect of our research indicates that new products do benefit from the development and testing of previous products. The results also indicate that pre-existing components/packages, including the common components/packages, undergo continuous change, but tend to sustain low fault densities. However, this is not always true for newly developed components/packages. Finally, the results also show that predictions of pre-release faults in the case of PolyFlow and post-release faults in the case of Eclipse can be done accurately from pre-release data, and furthermore, that these predictions benefit from information about additional products in the software product lines

Semi-supervised and Active Learning Models for Software Fault Prediction

As software continues to insinuate itself into nearly every aspect of our life, the quality of software has been an extremely important issue. Software Quality Assurance (SQA) is a process that ensures the development of high-quality software. It concerns the important problem of maintaining, monitoring, and developing quality software. Accurate detection of fault prone components in software projects is one of the most commonly practiced techniques that offer the path to high quality products without excessive assurance expenditures. This type of quality modeling requires the availability of software modules with known fault content developed in similar environment. However, collection of fault data at module level, particularly in new projects, is expensive and time-consuming. Semi-supervised learning and active learning offer solutions to this problem for learning from limited labeled data by utilizing inexpensive unlabeled data.;In this dissertation, we investigate semi-supervised learning and active learning approaches in the software fault prediction problem. The role of base learner in semi-supervised learning is discussed using several state-of-the-art supervised learners. Our results showed that semi-supervised learning with appropriate base learner leads to better performance in fault proneness prediction compared to supervised learning. In addition, incorporating pre-processing technique prior to semi-supervised learning provides a promising direction to further improving the prediction performance. Active learning, sharing the similar idea as semi-supervised learning in utilizing unlabeled data, requires human efforts for labeling fault proneness in its learning process. Empirical results showed that active learning supplemented by dimensionality reduction technique performs better than the supervised learning on release-based data sets

SUMP for Cities’ Sustainable Development

In a rapidly changing world, it is necessary to increase the engagement of local authorities and stakeholders to make urban mobility cleaner and more sustainable. The best way is to combine great ideas and innovative measures with political support. This Special Issue consists of six articles analyzing the impact of SUMPs. Innovative measures have been proposed to change urban transport systems towards sustainability: Chinese research has analyzed the tourist flow of Tibet using innovative technologies: mobile phone data, visualizations using GIS, and social networks. Lithuanian authors proposed three autonomous car travel development concepts that should become a conceptual tool in the development of ITS and C-ITS. An English scientific paper is based on a review of local transport policy documents from 13 cities in four countries. Most cities seek to reduce car travel as a proportion of trips. Experience from Slovenia shows that the comprehensive traffic calming approach has positive effects and contributes to achieving sustainable mobility. Korean researchers used the GINI coefficient to evaluate the bus system to identify bus nodes in order of importance. The last article described that multicriteria decision-making methods have been successfully used for assessing the effectiveness of sustainable transport systems, and a universal evaluation model was proposed

Using the gini coefficient for bug prediction in eclipse

The Gini coefficient is a prominent measure to quantify the inequality of a distribution. It is often used in the field of economy to describe how goods, e.g., wealth or farmland, are distributed among people. We use the Gini coefficient to measure code ownership by investigating how changes made to source code are distributed among the developer population. The results of our study with data from the Eclipse platform show that less bugs can be expected if a large share of all changes are accumulated, i.e., carried out, by relatively few developers

Fine-grained code changes and bugs: Improving bug prediction

Software development and, in particular, software maintenance are time consuming and require detailed knowledge of the structure and the past development activities of a software system. Limited resources and time constraints make the situation even more difficult. Therefore, a significant amount of research effort has been dedicated to learning software prediction models that allow project members to allocate and spend the limited resources efficiently on the (most) critical parts of their software system. Prominent examples are bug prediction models and change prediction models: Bug prediction models identify the bug-prone modules of a software system that should be tested with care; change prediction models identify modules that change frequently and in combination with other modules, i.e., they are change coupled. By combining statistical methods, data mining approaches, and machine learning techniques software prediction models provide a structured and analytical basis to make decisions.Researchers proposed a wide range of approaches to build effective prediction models that take into account multiple aspects of the software development process. They achieved especially good prediction performance, guiding developers towards those parts of their system where a large share of bugs can be expected. For that, they rely on change data provided by version control systems (VCS). However, due to the fact that current VCS track code changes only on file-level and textual basis most of those approaches suffer from coarse-grained and rather generic change information. More fine-grained change information, for instance, at the level of source code statements, and the type of changes, e.g., whether a method was renamed or a condition expression was changed, are often not taken into account. Therefore, investigating the development process and the evolution of software at a fine-grained change level has recently experienced an increasing attention in research.The key contribution of this thesis is to improve software prediction models by using fine-grained source code changes. Those changes are based on the abstract syntax tree structure of source code and allow us to track code changes at the fine-grained level of individual statements. We show with a series of empirical studies using the change history of open-source projects how prediction models can benefit in terms of prediction performance and prediction granularity from the more detailed change information.First, we compare fine-grained source code changes and code churn, i.e., lines modified, for bug prediction. The results with data from the Eclipse platform show that fine grained-source code changes significantly outperform code churn when classifying source files into bug- and not bug-prone, as well as when predicting the number of bugs in source files. Moreover, these results give more insights about the relation of individual types of code changes, e.g., method declaration changes and bugs. For instance, in our dataset method declaration changes exhibit a stronger correlation with the number of bugs than class declaration changes.Second, we leverage fine-grained source code changes to predict bugs at method-level. This is beneficial as files can grow arbitrarily large. Hence, if bugs are predicted at the level of files a developer needs to manually inspect all methods of a file one by one until a particular bug is located.Third, we build models using source code properties, e.g., complexity, to predict whether a source file will be affected by a certain type of code change. Predicting the type of changes is of practical interest, for instance, in the context of software testing as different change types require different levels of testing: While for small statement changes local unit-tests are mostly sufficient, API changes, e.g., method declaration changes, might require system-wide integration-tests which are more expensive. Hence, knowing (in advance) which types of changes will most likely occur in a source file can help to better plan and develop tests, and, in case of limited resources, prioritize among different types of testing.Finally, to assist developers in bug triaging we compute prediction models based on the attributes of a bug report that can be used to estimate whether a bug will be fixed fast or whether it will take more time for resolution.The results and findings of this thesis give evidence that fine-grained source code changes can improve software prediction models to provide more accurate results