A Case Study on Improving the Software Dependability of a ROS Path Planner for Steep Slope Vineyards

Software for robotic systems is becoming progressively more complex despite the existence of established software ecosystems like ROS, as the problems we delegate to robots become more and more challenging. Ensuring that the software works as intended is a crucial (but not trivial) task, although proper quality assurance processes are rarely seen in the open-source robotics community. This paper explains how we analyzed and improved a specialized path planner for steep-slope vineyards regarding its software dependability. The analysis revealed previously unknown bugs in the system, with a relatively low property specification effort. We argue that the benefits of similar quality assurance processes far outweigh the costs and should be more widespread in the robotics domain.

A Fuzzy Logic-Based Model for Classifying Software Modules in Order to Achieve Dependable Software

In this study, a fuzzy logic-based framework has been proposed to predict the situation of the software modules in the earlier phases of software lifecycle. The proposed model has taken into account domain experts' opinions and the available state of different software metrics as inputs. On the basis of dependability measures of software, different modules have been ranked earlier in the development process. Effect of the modules on the reliability, security, and availability of software has been judged by the proposed technique based on Mahalanobis distance metric. The study of software dependability in early phase assists the software developers to take corrective actions, which leads to minimize the testing efforts as well as development time. The proposed technique has been implemented on the promise software engineering repository data set. Performance of the proposed methodology is promising in identifying the fault-prone software modules. The result has also been compared with some known methodologies.