Visual Debugging of Behavioural Models

International audienceIn this paper, we present the CLEAR visualizer tool, which supports the debugging task of behavioural models being analyzed using model checking techniques. The tool provides visualization techniques for simplifying the comprehension of counterexamples by highlighting some specific states in the model where a choice is possible between executing a correct behaviour or falling into an erroneous part of the model. Our tool was applied successfully to many case studies and allowed us to visually identify several kinds of typical bugs. Video URL: https://youtu.be/nJLOnRaPe1A

A comparative evaluation of dynamic visualisation tools

Despite their potential applications in software comprehension, it appears that dynamic visualisation tools are seldom used outside the research laboratory. This paper presents an empirical evaluation of five dynamic visualisation tools - AVID, Jinsight, jRMTool, Together ControlCenter diagrams and Together ControlCenter debugger. The tools were evaluated on a number of general software comprehension and specific reverse engineering tasks using the HotDraw objectoriented framework. The tasks considered typical comprehension issues, including identification of software structure and behaviour, design pattern extraction, extensibility potential, maintenance issues, functionality location, and runtime load. The results revealed that the level of abstraction employed by a tool affects its success in different tasks, and that tools were more successful in addressing specific reverse engineering tasks than general software comprehension activities. It was found that no one tool performs well in all tasks, and some tasks were beyond the capabilities of all five tools. This paper concludes with suggestions for improving the efficacy of such tools

ACE 16k based stand-alone system for real-time pre-processing tasks

This paper describes the design of a programmable stand-alone system for real time vision pre-processing tasks. The system's architecture has been implemented and tested using an ACE16k chip and a Xilinx xc4028xl FPGA. The ACE16k chip consists basically of an array of 128×128 identical mixed-signal processing units, locally interacting, which operate in accordance with single instruction multiple data (SIMD) computing architectures and has been designed for high speed image pre-processing tasks requiring moderate accuracy levels (7 bits). The input images are acquired using the optical input capabilities of the ACE16k chip, and after being processed according to a programmed algorithm, the images are represented at real time on a TFT screen. The system is designed to store and run different algorithms and to allow changes and improvements. Its main board includes a digital core, implemented on a Xilinx 4028 Series FPGA, which comprises a custom programmable Control Unit, a digital monochrome PAL video generator and an image memory selector. Video SRAM chips are included to store and access images processed by the ACE16k. Two daughter boards hold the program SRAM and a video DAC-mixer card is used to generate composite analog video signal.European Commission IST2001 – 38097Ministerio de Ciencia y Tecnología TIC2003 – 09817- C02 – 01Office of Naval Research (USA) N00014021088

Towards a mood sensitive integrated development environment to enhance the performance of programmers

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The aim of the research was to analyze the possibility of developing an Integrated Development Environment (IDE) that could improve a programmer‘s performance by considering their current mood. Various experiments were conducted to study this idea. However, the impact of moods on programmer performance was initially examined in the literature. Based on this, a Cognitive Programming Task Model (CPTM) was developed showing that various cognitive functions and programming activities are interrelated. A second model derived from the literature, the Cognitive Mood Model (CMM), suggested that moods are also interrelated with various cognitive functions. Combining these two models indirectly suggests a relation between moods and programming tasks, which was presented as the Mood Programming Model (MPM). As direct empirical support was lacking for this relation, two experiments were conducted to study the effect mood could have on performance in a debug task. Validated mood-inducing movie clips were used to induce specific moods along two-mood dimensions: valence and arousal. The first study was conducted online. The results showed that arousal is a significant factor when considering programmer performance whereas valence was found to have no significant effect. The second study was a continuation study to validate the findings from the first study within lab conditions. The results were not able to confirm the findings of the first experiment. The reasons for these findings were explained accordingly. As mood was found to have an effect on a programmer‘s coding and debugging performance, this factor might be considered when developing a support system. The next step in the research was therefore to consider mood measuring in a non-interruptive way. The next two experiments were based around the hypothesis that ―moods can be measured from the keyboard and mouse interaction of the computer user‖. In the first experiment an application was installed on participants‘ computers to record their key presses and mouse clicks in a log file. Their self reported moods in intervals of 20 minutes were also stored in the same file over an average period of eight days. Correlations between participants‘ self reported moods and their keyboard and mouse use revealed that it might be possible to measure moods of the some of the participants. The second experiment took place in the lab, where participants were asked to perform programming like tasks while listening to ii mood inducing background music. Their moods were measured with a Galvanic Skin Response (GSR) meter whereas key presses and mouse clicks again were recorded in log files. The correlations between GSR measurements and keyboard and mouse interaction validated the findings of the experiment in the field that it might be possible to measure the mood of some users from their computer use. Analyzing participants‘ personality traits showed dutifulness and self discipline as indicators that a person‘s mood correlates with his/her interaction behaviour. Considering that mood has an effect on programmer performance and that it might be possible to measure mood in a non-intrusive manner, the last question to focus on was whether a computer-generated intervention could change a programmer‘s mood and consequently improve their performance. In the final experiment programmers had to dry run algorithms for 16 minutes with the expectation that a level of boredom would set in. After this the video clip instructed them to participate in some physical exercises. Participants continued tracing algorithms for 8 minutes after the intervention. Results showed that the mood change after the intervention coincided with a programmers improved ability to provide the correct output of the algorithms. Together these findings lay the foundation for developing an IDE that can measure the programmer mood in a non-intrusive way and make effective interventions to improve programmer performance