Khalil Shihab

Incorporating Software Visualization in the Design of Intelligent Diagnosis Systems for User Programming

Program diagnosis systems were developed to help users solve programming problems. By providing guidence on errors and misconceptions, these systems can help the users in writing programs and understanding their dynamic behavior. Features of software visualization which aim at providing visual and concrete depictions to the abstractions and operations of programs have also shown to be making programs more understandable. The main theme of this paper is to asses the usefulness of incorporating features of software visualization into the design of program diagnosis systems intended for novices. We report an empirical evaluation to assess the effectiveness of supporting visualization features during problem solving. The system used in the evaluation integrates visualzation and immediacy features and supports a model-tracing based approach to program diagnosis. Unlike other similar systems, our prototype system supports a more flexible style of interaction by increasing the grain size of diagnosis to a complete programming statement. The evaluation reported here seems to suggest that when supported with visualization features, systems for program diagnosis tend to be more effective in helping the users during problem solving.

Automatic Detection of Performance Bottlenecks Using a Case-Based Reasoning Approach

Al though the p rocess of de tec t ing and resolv ing p e r f o r m a n c e bo t t l enecks o f c o m p u t e r sys tems is t i m e c o n s u m i n g and c o m p l e x , it has recent ly b e c o m e more impor tant and popu la r issue because of the increase in the complex i ty and the divers i ty o f these sys tems. Tradi t ional ly , this p roces s has been implemented us ing ru le -based sys tems. We, however , noted that case -based reason ing is more e f f ic ien t and o f great benef i t in this area . T h i s is main ly because non-exper t users o f p e r f o r m a n c e m o d e l i n g or mon i to r i ng so f tware , both in p repar ing inputs and in terpre t ing outputs , tend to use old cases by assoc ia t ing c o m p u t e r sys tems that reveal s imilar p e r f o r m a n c e charac ter is t ics . It is a lso because ru le -based sys tems are less modu la r than case -based sys tems in that a d d i n g (or r e m o v i n g ) a new case in a ca se -based sys tem d o e s not a f fec t any exis t ing case. In this work , we present an app l ica t ion o f our integrated app roach to the d iagnos i s of bo t t lenecks in c o m p u t e r sys t ems in o rde r to p rov ide the necessary r emed ie s to ach ieve accep t ab l e p e r f o r m a n c e . T h e under ly ing t echn ique uses the p e r f o r m a n c e moni to r va lues to f ind the mos t poss ib le a s s ignment to the work load and system pa rame te r s . It is based on case -based r eason ing ut i l izing fuzzy set concep t s to conver t the quant i ta t ive a t t r ibutes into qual i ta t ive t e rms for index ing and ret r ieval . T h e app roach a lso shows that fuzzy indexing and retr ieval a re use fu l in the c o m p u t e r p e r f o r m a n c e doma in . Reprint requests to: Khalil Shihab. Dept. of Computer Science Sultan Qaboos University, PO Box 36 Muscat 123. Oman: e-mail: kshihab@squ.edu.om: *haider@squ.edu.om

ACTIVE vs. PASSIVE APPROACHES TO INTELLIGENT PROGRAM DIAGNOSIS

Much of research in intelligent programming systems for users has been polarized towards two opposite domains: active and passive approaches to diagnosis. The advocates of the active approach claim that much of the effectiveness of intelligent program systems is contributed to having strong control over the behavior of the users and providing immediate feedback on errors and misconceptions. Opponents of this approach, on the other hand, have argued that active approach through its interventionist style does not provide users the flexibility needed to observe their own behavior and discover their own errors, hence the users are not given an opportunity to selfdebug their solutions. This paper covers the engineering of intelligent program diagnosis systems and reports an empirical evaluation which attempts to get some insights into the superiority of active approach over passive approach or vice versa. The evaluation is conducted using our prototype system DISCOVER. The system provides a visualization-based environment which supports both active and passive modes of intelligent program diagnosis.

Use of a Bayesian network decision tool for water quality assessment

Groundwater resources in Oman are considered very precious and play a great role in the economical development. However, groundwater contamination is one of the major concerns facing the country and, therefore, it needs an accurate measurement technique. In this work, the Bayesian Technique is applied to groundwater quality data sets obtained from various locations in the Salalah area to the south of Oman. This technique emphasizes not only the p robabilistic dependencies between pollutants but also the precision and the accuracy of the tested methods used by environmental laboratories. First, we present a new technique for data preprocessing. Then we describe the network models we developed, as well as the methods used to build these models. Various challenges, such as acquiring groundwater datasets, identifying pollutants and anticipating potential problem contaminants , are addressed. Finally, we present the results of applications of these models.

A Theme-based Search Technique

The current search engines usually return a large number of irrelevant documents for a certain query. As a result, accessing such information and filtering out these documents can cause frustration and often result in waste of time and effort for the users while surfing the web. This is mainly because of the underlying techniques used in these engines. These techniques are mostly based in the frequency of the keywords of the query in the HTML code. In addition, issues such as dealing with classifying the pages found for a query according to previous visits along with features needed to make intelligent decisions regarding the access patterns of the users are not considered. This work presents an intelligent search engine, called ORCA that returns the most relevant documents for users queries. This search engine analyses the queries and builds themes (models) to be used when the engine is confronted with similar queries. The intelligent component is used for constructing a model of the user behavior and using that model to fetch and even prefetch information and documents considered of interest to the user. It uses both latent semantic analysis and web page feature selection for clustering web pages. Latent semantic analysis is used to find the semantic relations between keywords, and between documents.

Probabilistic Models for Assessing the Impact of Salinization and Chemical Pollutants

Development and rapid population growth have impacted Oman’s water resources significantly. Increasing the degradation of groundwater quality by salinization and chemical contaminants threaten pri mary sources of drinking water, especiall y in the coastal agricultura l areas. Hence, there is a substantial demand in the country for water conservation technology. This work elaborates the quality deterioration of groundwater due to chemical contaminants, which are the pri me environmental issue of the Salalah region to the south of Oman. First, we describe the develop ment and application of Dynamic Bayesian Networks (DBNs) combine with a preprocessing Bayesian technique to deter mine the impact of these contaminants on groundwater quality. These paradigm address the probabilisti c and dynamic characteristics that are significant in the understanding of pollution generation from various information sources, but in a fashion which manages the uncertainties in these sources. Second, we discuss and compare the results produced by these methods with that produced by the application of classical ti me series models.