Anders Ardö

Recovering from a decade: a systematic mapping of information retrieval approaches to software traceability

Engineers in large-scale software development have to manage large amounts of information, spread across many artifacts. Several researchers have proposed expressing retrieval of trace links among artifacts, i.e. trace recovery, as an Information Retrieval (IR) problem. The objective of this study is to produce a map of work on IR-based trace recovery, with a particular focus on previous evaluations and strength of evidence. We conducted a systematic mapping of IR-based trace recovery. Of the 79 publications classified, a majority applied algebraic IR models. While a set of studies on students indicate that IR-based trace recovery tools support certain work tasks, most previous studies do not go beyond reporting precision and recall of candidate trace links from evaluations using datasets containing less than 500 artifacts. Our review identified a need of industrial case studies. Furthermore, we conclude that the overall quality of reporting should be improved regarding both context and tool details, measures reported, and use of IR terminology. Finally, based on our empirical findings, we present suggestions on how to advance research on IR-based trace recovery.

Importance of HTML structural elements and metadata in automated subject classification

The aim of the study was to determine how significance indicators assigned to different Web page elements (internal metadata, title, headings, and main text) influence automated classification. The data collection that was used comprised 1000 Web pages in engineering, to which Engineering Information classes had been manually assigned. The significance indicators were derived using several different methods: (total and partial) precision and recall, semantic distance and multiple regression. It was shown that for best results all the elements have to be included in the classification process. The exact way of combining the significance indicators turned out not to be overly important: using the F1 measure, the best combination of significance indicators yielded no more than 3% higher performance results than the baseline.

Browsing and searching behavior in the Renardus Web service: a study based on log analysis

Renardus is a distributed Web-based service, which provides integrated searching and browsing access to quality-controlled Web resources. With the overall purpose of improving Renardus, the research aims to study: the detailed usage patterns (quantitative/qualitative, paths through the system); the balance between browsing and searching or mixed activities; typical sequences of usage steps and transition probabilities in a session; typical entry points, referring sites, points of failure and exit points; and, the usage degree of the browsing support features.

Users Browsing Behaviour in a DDC-Based Web Service: A Log Analysis

SUMMARY This study explores the navigation behaviour of all users of a large web service, Renardus, using web log analysis. Renardus provides integrated searching and browsing access to quality-controlled web resources from major individual subject gateway services. The main navigation feature is subject browsing through the Dewey Decimal Classification (DDC) based on mapping of classes of resources from the distributed gateways to the DDC structure. Among the more surprising results are the hugely dominant share of browsing activities, the good use of browsing support features like the graphical fish-eye overviews, rather long and varied navigation sequences, as well as extensive hierarchical directory-style browsing through the large DDC system.

Hardware support for efficient execution of Ada tasking

Several aspects of run-time support for Ada that contribute to the efficiency of the implementation are discussed. To fully support Ada, including efficient tasking, hardware support is needed. A simple method for identifying the parts of the run-time system that are good candidates for hardware support is described and used to identify three areas in which support is needed. Two kinds of smart queue memory are proposed and shown to give substantial support for Ada implementation. >

Effectiveness of caching in a distributed digital library system

Today independent publishers are offering digital libraries with fulltext archives. In an attempt to provide a single user-interface to a large set of archives, the studied Article-Database-Service offers a consolidated interface to a geographically distributed set of archives. While this approach offers a tremendous functional advantage to a user, the fulltext download delays caused by the network and queuing in servers make the user-perceived interactive performance poor. This paper studies how effective caching of articles at the client level can be achieved as well as at intermediate points as manifested by gateways that implement the interfaces to the many fulltext archives. A central research question in this approach is: What is the nature of locality in the user access stream to such a digital library? Based on access logs that drive the simulations, it is shown that client-side caching can result in a 20% hit rate. Even at the gateway level temporal locality is observable, but published replacement algorithms are unable to exploit this temporal locality. Additionally, spatial locality can be exploited by considering loading into cache all articles in an issue, volume, or journal, if a single article is accessed. But our experiments showed that improvement introduced a lot of overhead. Finally, it is shown that the reason for this cache behavior is the long time distance between re-accesses, which makes caching quite unfeasible.

An Evaluation of Document Prefetching in a Distributed Digital Library

Latency is a fundamental problem for all distributed systems including digital libraries. To reduce user perceived delays both caching – keeping accessed objects for future use – and prefetching – transferring objects ahead of access time – can be used. In a previous paper we have reported that caching is not worthwhile for digital libraries due to low re-access frequencies.

Can We Trust Web Page Metadata

A statistical study of embedded metadata in a sample of more than 4 million HTML Web pages is reported. The researchers try to determine and quantify the validity of this metadata. Of particular interest is to see if the metadata are trustworthy enough for determining the topic of a Web page. Datasets are collected by a Web crawler running as both a general and a focused crawler. Metadata fields “title,” “author,” “keywords,” “description,” and “language” are analyzed in detail together with Dublin Core metadata. The study reveals problems with how metadata are created. Among the 75% of all Web pages that have interesting metadata, the field “language” is the most trustworthy. All other metadata fields show a high degree of duplication thus degrading their usefulness. The strict answer to the title question is no, however, there is a lot of meaningful and useful information, but it must be interpreted and used with care. The study provides statistics on the usage of metadata today and how it has changed over time.

Automated Classification of Textual Documents Based on a Controlled Vocabulary in Engineering

Automated subject classification has been a challenging research issue for many years now, receiving particular attention in the past decade due to rapid increase of digital documents. The most frequent approach to automated classification is machine learning. It, however, requires training documents and performs well on new documents only if these are similar enough to the former. We explore a string-matching algorithm based on a controlled vocabulary, which does not require training documents--instead it reuses the intellectual work put into creating the controlled vocabulary. Terms from the Engineering Information thesaurus and classification scheme were matched against title and abstract of engineering papers from the Compendex database. Simple string-matching was enhanced by several methods such as term weighting schemes and cut-offs, exclusion of certain terms, and enrichment of the controlled vocabulary with automatically extracted terms. The best results are 76% recall when the controlled vocabulary is enriched with new terms, and 79% precision when certain terms are excluded. Precision of individual classes is up to 98%. These results are comparable to state-of-the-art machine-learning algorithms.

Implementation of a Pascal based parallel language for a multiprocessor computer

This paper reports the work on implementing a parallel version of Pascal on a small scale multiprocessor computer. A few simple primitives were included to support parallel programming. The code generation, linking and loading procedures are described. An overview of the hardware is included. Finally some programming experience with the system is reported.