Hazeline U. Asuncion

Software traceability with topic modeling

Software traceability is a fundamentally important task in software engineering. The need for automated traceability increases as projects become more complex and as the number of artifacts increases. We propose an automated technique that combines traceability with a machine learning technique known as topic modeling. Our approach automatically records traceability links during the software development process and learns a probabilistic topic model over artifacts. The learned model allows for the semantic categorization of artifacts and the topical visualization of the software system. To test our approach, we have implemented several tools: an artifact search tool combining keyword-based search and topic modeling, a recording tool that performs prospective traceability, and a visualization tool that allows one to navigate the software architecture and view semantic topics associated with relevant artifacts and architectural components. We apply our approach to several data sets and discuss how topic modeling enhances software traceability, and vice versa.

An end-to-end industrial software traceability tool

Traceability is an important aspect of software development that is often required by various professional standards and government agencies. Yet current industrial approaches do not typically address end-to-end traceability. Moreover, many industry projects become entangled in process overhead and fail to derive much benefit from current traceability solutions. This paper presents a successful end-to-end software traceability tool developed at Wonderware, a software development company and a business unit of Invensys Systems, Inc. This process-oriented approach achieves comprehensive traceability and supports the entire software development life cycle by focusing on both requirements traceability and process traceability. We offer new perspectives in analyzing the problem as well as general traceability guidelines. These guidelines have emerged from the experience of implementing and deploying the traceability tool within actual company constraints. We discuss encouraging results and point to the advantages gained in using our approach.

ArchStudio 4: An Architecture-Based Meta-Modeling Environment

We will demonstrate ArchStudio, an environment for software architecture modeling and meta-modeling. We will also showcase a set of innovative architecture-centric applications that use ArchStudio technologies as their basis.

Intellectual Property Rights Requirements for Heterogeneously-Licensed Systems

Heterogeneously-licensed systems pose new challenges to analysts and system architects. Appropriate intellectual property rights must be available for the installed system, but without unnecessarily restricting other requirements, the system architecture, and the choice of components both initially and as it evolves. Such systems are increasingly common and important in e-business, game development, and other domains. Our semantic parameterization analysis of open-source licenses confirms that while most licenses present few roadblocks, reciprocal licenses such as the GNU General Public License produce knotty constraints that cannot be effectively managed without analysis of the systems license architecture. Our automated tool supports intellectual property requirements management and license architecture evolution. We validate our approach on an existing heterogeneously-licensed system.

Analyzing software licenses in open architecture software systems

A substantial number of enterprises and independent software vendors are adopting a strategy in which software-intensive systems are developed with an open architecture (OA) that may contain open source software (OSS) components or components with open APIs. The emerging challenge is to realize the benefits of openness when components are subject to different copyright or property licenses. In this position paper, we identify key properties of OSS licenses, present a license analysis scheme, and discuss our approach for automatically analyzing license interactions.

Capturing custom link semantics among heterogeneous artifacts and tools

Automated techniques aid in minimizing the overhead associated with the capture and maintenance of trace links. However, many challenges to automated traceability remain, such as linking heterogeneous artifacts and capturing custom link semantics. In this position paper, we propose a combination of techniques, including prospective link capture, open hypermedia, and rules, in order to address these challenges and complement current automated techniques. Our approach borrows ideas from e-Science, a domain in which tracing data plays a crucial role in the repeatability of experiments.

Automated Techniques for Capturing Custom Traceability Links Across Heterogeneous Artifacts

Software traceability is becoming an increasingly important facet of software engineering, especially as development projects become more distributed, decentralized, and dependent on third-party software. This chapter illustrates how traceability links can be automatically captured in situ, while software engineers perform their development tasks, through the use of specialized open hypermedia adapters and custom rules. Focusing on the mobile phone case study, we demonstrate how users can integrate their custom filters, heuristics, and relationship types, as well as their existing development tools, into our traceability system. We also show how our traceability link-capture technique can be incorporated with search tools and how traceability links can be effectively maintained. We provide practical usage scenarios and brief technical discussions for each of these capabilities.

In Situ Data Provenance Capture in Spreadsheets

The capture of data provenance is a fundamentally important task in eScience. While provenance can be captured using techniques such as scientific workflows, typically these techniques do not trace internal data manipulations that occur within off-the-shelf analysis tools. Yet it is still essential to capture data provenance within such environments. This paper discusses an in situ provenance approach for spreadsheet data in MS Excel, a commonly used analysis environment among scientists. We describe the design and implementation of an Excel tool that captures provenance unobtrusively in the background, allows for user annotations, provides undo/redo functionality at various levels of task granularity, and presents the captured provenance in an accessible format to support a range of provenance queries for analysis. We also present several motivating use case scenarios and a user evaluation which suggests that our approach is both efficient and useful to scientists.

Automated data provenance capture in spreadsheets, with case studies

One of the most important tasks in eScience is capturing the provenance of data. While scientists frequently use off-the-shelf analysis tools to process and manipulate data, current provenance techniques such as those based on scientific workflows are typically not able to trace internal data manipulations that occur within these tools. In this paper, we focus on one such off-the-shelf tool, MS Excel, which is used by many scientists; specifically, we propose InSituTrac, an automated in situ provenance approach for spreadsheet data in Excel. Our framework captures data provenance unobtrusively in the background, allows for user annotations, provides undo/redo functionality at various levels of granularity, presents the captured provenance in an accessible format, and visualizes captured provenance to support analysis of the provenance log. We highlight several motivating use case scenarios which show how provenance queries can be answered by our approach. Finally, case studies with an atmospheric science research group and a fisheries research group suggest that the automated provenance approach is both efficient and useful to scientists.

A Multi-level Funneling Approach to Data Provenance Reconstruction

When data are retrieved from a file storage system or the Internet, is there information about their provenance (i.e., their origin or history)? It is possible that data could have been copied from another source and then transformed. Often, provenance is not readily available for data sets created in the past. Solving such a problem is the motivation behind the 2014 Provenance Reconstruction Challenge. This challenge is aimed at recovering lost provenance for two data sets: one data set (WikiNews articles) in which a list of possible sources has been provided, and another data set (files from GitHub repositories) in which the file sources are not provided. To address this challenge, we present a multi-level funneling approach to provenance reconstruction, a technique that incorporates text processing techniques from different disciplines to approximate the provenance of a given data set. We built three prototypes using this technique and evaluated them using precision and recall metrics. Our preliminary results indicate that our technique is capable of reconstructing some of the lost provenance.