Software requirement analysis is an essential step in software development process, which defines what is to be built in a project. Requirements are mostly written in text and will later evolve to fine-grained and actionable artifacts with details about system configurations, technology stacks, etc. Tracing the evolution of requirements enables stakeholders to determine the origin of each requirement and understand how well the software's design reflects to its requirements. Reckoning requirements traceability is not a trivial task, we focus on applying machine learning approach to classify traceability between various associated requirements. In particular, we investigate a 2-learner, ontology-based approach, where we train two classifiers to separately exploit two types of features, lexical features and features derived from a hand-built ontology. In comparison to a supervised baseline system that uses only lexical features, our approach yields a relative error reduction of 25.9%. Most interestingly, results do not deteriorate when the hand-built ontology is replaced with its automatically constructed counterpart.

We present a new method to identify navigation-related Web usability problems based on comparing actual and anticipated usage patterns. The actual usage patterns can be extracted from Web server logs routinely recorded for operational websites by first processing the log data to identify users, user sessions, and user task-oriented transactions, and then applying an usage mining algorithm to discover patterns among actual usage paths. The anticipated usage, including information about both the path and time required for user-oriented tasks, is captured by our ideal user interactive path models constructed by cognitive experts based on their cognition of user behavior. The comparison is performed via the mechanism of test oracle for checking results and identifying user navigation difficulties. The deviation data produced from this comparison can help us discover usability issues and suggest corrective actions to improve usability. A software tool was developed to automate a significant part of the activities involved. With an experiment on a small service-oriented website, we identified usability problems, which were cross-validated by domain experts, and quantified usability improvement by the higher task success rate and lower time and effort for given tasks after suggested corrections were implemented. This case study provides an initial validation of the applicability and effectiveness of our method.

Orthogonal Defect Classification (ODC), the most influential framework for software defect classification and analysis, provides valuable in-process feedback to system development and maintenance. Conducting ODC classification on existing organizational defect reports is human intensive and requires experts' knowledge of both ODC and system domains. This paper presents AutoODC, an approach and tool for automating ODC classification by casting it as a supervised text classification problem. Rather than merely apply the standard machine learning framework to this task, we seek to acquire a better ODC classification system by integrating experts' ODC experience and domain knowledge into the learning process via proposing a novel Relevance Annotation Framework. We evaluated AutoODC on an industrial defect report from the social network domain. AutoODC is a promising approach: not only does it leverage minimal human effort beyond the human annotations typically required by standard machine learning approaches, but it achieves an overall accuracy of 80.2% when using manual classifications as a basis of comparison.