Software testing involves verification and validation of the software to meet the requirements elucidated by customers in the earlier phases and to subsequently increase software reliability. Around half of the resources, such as manpower and CPU time are consumed and a major portion of the total cost of developing the software is incurred in testing phase, making it the most crucial and time-consuming phase of a software development lifecycle (SDLC). Also the fault detection process (FDP) and fault correction process (FCP) are the important processes in SDLC. A number of software reliability growth models (SRGM) have been proposed in the last four decades to capture the time lag between detected and corrected faults. But most of the models are discussed under static environment. The purpose of this paper is to allocate the resources in an optimal manner to minimize the cost during testing phase using FDP and FCP under dynamic environment. An elaborate optimization policy based on optimal control theory for resource allocation with the objective to minimize the cost is proposed. Further, genetic algorithm is applied to obtain the optimum value of detection and correction efforts which minimizes the cost. Numerical example is given in support of the above theoretical result. The experimental results help the project manager to identify the contribution of model parameters and their weight.

With the growing competition and the demand of the customers, a software organization needs to regularly provide up-gradations and add features to its existing version of software. For the organization, creating these software upgrades means an increase in the complexity of the software which in turn leads to the increase in the number of faults. Also, the faults left undetected in the previous version need to be addressed in this phase. Many software reliability growth models have been proposed to model the phenomenon of multi-release problems using two stage failure observation and correction processes. The model proposed in this paper partitions the fault removal process into a two-stage process which includes fault detection process and fault removal process considering the joint effect of premeditated release pressure and resource restrictions using a well-known Cobb–Douglas production function for the multi release problem of a software. The faults detected in the operational phase of the previous release or left incomplete are also incorporated in the next release. A generalized framework for the multi-release problem in which fault detection follows an exponential distribution function and fault correction follows Gamma distribution function is proposed and verified on a real data set of four releases of software. The estimated parameters and comparison criteria are also given.

Automotive industries are implementing high-end technology with minimalistic design and advancing rapidly. Tesla Model 3 is among those automobiles replaced physical knobs with fully functional touchscreen screens to enhance the minimal interior aesthetic. All the in-vehicle touchscreen interaction requires visual attention allocation between driving and touchscreen interaction resulting in drivers' divided attention posing a hazard or risk to the driver's safety. This research aims to assess the impact of the vertical grid design of the Tesla Model 3 infotainment system on visual search time while multitasking. For comparison, a horizontal grid design was prototyped to see the difference in visual search time between vertical and horizontal grid designs. Task success rate, the number of incorrect searches, reaction time for search, and subjective measures were considered to assess the dimension of efficiency and satisfaction. Eleven novice participants performed visual search tasks and answered follow-up questions based upon the task experience. Visual search task was designed using Psycho-Py and to collect real-time data Pavlovia.org online platform was used to run the experiment. Each trial in the visual search tasks starts with a red color fixation cross presented on a grey background lasting on-screen for 500 milliseconds. This fixation cross was followed by an on-road driving video that contains a question presented on the top-center of the video display. An image of the infotainment system appears at the bottom right corner of the screen at different time stamps lasting for 2 seconds. When a participant presses the right or left key based on the response, the video automatically terminates. This is followed by a two-response window, one after the other; the first response window where the participant must type the visual searched target answer asked during the video. In the second response window, the participant must typecast the total number of cars counted. A fixation cross appears again for 500 milliseconds indicating to participants that the subsequent trial will begin after this. After task completion, follow-up questions were asked. The purpose of asking follow-up questions was to understand the frustrations and difficulty experienced by the participants while searching target on the infotainment screen. A mixture of quantitative and qualitative data was gathered to assess the goals of efficiency and satisfaction. The result indicates that task success rate or target miss rate, and incorrect task response are high in the vertical grid design of the Tesla Model 3 infotainment system. Visual search time for vertical grid conditions was significantly higher than the NHTSA guideline. The results showed that the horizontal grid design strategy leads to a better target visual search user experience. The study concludes that the Tesla Model 3 infotainment system lacks discoverability, goal-based design, affordance, visual momentum, mode awareness, and consistency with the user's mental model. A robust design is required to achieve the crucial information search without leading the driver to a high risk of causing adverse consequences due to interface design. This research was conducted and submitted to the faculty of the graduate program in Human Factors/ Ergonomics for the partial fulfillment of the requirements for the Degree Master of Science.Keywords: visual attention, NHTSA guideline, discoverability, affordance, visual momentum, mode awareness, mental model