Cara Stein

A comparison of cohesion metrics for object-oriented systems

Abstract Cohesion is the degree to which the elements of a class or object belong together. Many different object-oriented cohesion metrics have been developed; many of them are based on the notion of degree of similarity of methods. No consensus has yet arisen as to which of these metrics best measures cohesion; this is a problem for software developers since there are so many suggested metrics, it is difficult to make an informed choice. This research compares various cohesion metrics with ratings of two separate teams of experts over two software packages, to determine which of these metrics best match human-oriented views of cohesion. Additionally, the metrics are compared statistically, to determine which tend to measure the same kinds of cohesion. Differences in results for different object-oriented metrics tools are discussed.

Computing software metrics from design documents

Software metrics can provide an automated way for software practitioners to assess the quality of their software. The earlier in the software development lifecycle this information is available, the more valuable it is, since changes are much more expensive to make later in the lifecycle.Semantic metrics, introduced by Etzkorn and Delugach, assess software according to the meaning of the softwares functionality in its domain. This is in contrast to traditional metrics, which use syntax measures to assess code. Because semantic metrics do not rely on the syntax or structure of code, they can be computed from requirements or design specifications before the system has been implemented. This paper focuses on using semantic metrics to assess systems that have not yet been implemented.

On-board Data Mining

Networks of remote sensors are becoming more common as technology improves and costs decline. In the past, a remote sensor was usually a device that collected data to be retrieved at a later time by some other mechanism. This collected data were usually processed well after the fact at a computer greatly removed from the in situ sensing location. This has begun to change as sensor technology, on-board processing, and network communication capabilities have increased and their prices have dropped. There has been an explosion in the number of sensors and sensing devices, not just around the world, but literally throughout the solar system. These sensors are not only becoming vastly more sophisticated, accurate, and detailed in the data they gather but they are also becoming cheaper, lighter, and smaller. At the same time, engineers have developed improved methods to embed computing systems, memory, storage, and communication capabilities into the platforms that host these sensors. Now, it is not unusual to see large networks of sensors working in cooperation with one another. Nor does it seem strange to see the autonomous operation of sensorbased systems, from space-based satellites to smart vacuum cleaners that keep our homes clean and robotic toys that help to entertain and educate our children. But access to sensor data and computing power is only part of the story. For all the power of these systems, there are still substantial limits to what they can accomplish. These include the well-known limits to current Artificial Intelligence capabilities and our limited ability to program the abstract concepts, goals, and improvisation needed for fully autonomous systems. But it also includes much more basic engineering problems such as lack of adequate power, communications bandwidth, and memory, as well as problems with the geolocation and real-time georeferencing required to integrate data from multiple sensors to be used together.

SEMANTIC METRICS: METRICS BASED ON SEMANTIC ASPECTS OF SOFTWARE

Software practitioners need ways to assess their software, and metrics can provide an automated way to do that, providing valuable feedback with little effort earlier than the testing phase. Semantic metrics were proposed to quantify aspects of software quality based on the meaning of softwares task in the domain. Unlike traditional software metrics, semantic metrics do not rely on code syntax. Instead, semantic metrics are calculated from domain information, using the knowledge base of a program understanding system. Because semantic metrics do not rely on code syntax, they can be calculated before code is fully implemented. This article evaluates the semantic metrics theoretically and empirically. We find that the semantic metrics compare well to existing metrics and show promise as early indicators of software quality.

Visualizations for the spyglass ontology-based information analysis and retrieval system

Spyglass is an ontology-based information retrieval system designed to help analysts explore very large collections of unstructured text documents. The tool includes two main components: server and client. The server is a web-based service that uses a specific domain ontology to index a collection of documents, answer queries from the client, and provide retrieval and visualization services based on the ontology and the resulting index. The client is a graphical user interface which allows analysts to explore the document collections, query single or multiple entities of interest of the ontology and retrieve the documents relevant to the query. The rich set of visualization tools in Spyglass will be presented in this paper.