Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E

Replan: Release planning for agile development

Release Planning methodologies have made possible that project managers and users in general can plan project’s releases. These methods try to automatize the human-based planning processes. Currently they are a few web-based and stand-alone tools about release planning, but not all of them offer the same functionalities, like the update of an already planned release or a detailed plan expressed in a timeline. Moreover, these systems are oriented to stakeholders criteria, without taking enough consideration to the available resources. This becomes a limitation, because in many occasions it is vital to have a temporal planning of a release. It also affects key aspects like the planning efficiency or the speed at which it is executed. In this project a web-based release planning tool has been developed. In this tool, users can create a release with different entities in an easy and simple way. The tool is based in a mathematical model that generates an scheduled plan as tight as possible to the available time and resources. On the other hand, the tool also guarantees the priority fulfillment of features, by respecting the temporal criteria that the user could establish. The system is also modular, as it can be integrated with other possible different visualizations. Its development in a cloud server also provides public access and scalability. The tests performed to the system show that the presented mathematical model guarantees the scheduled and efficient planning of a project’s release

Software release planning

One of the most critical activities in software product development is the decisional process that assigns features to subsequent releases under technical, resource, risk, and budget constraints. This decision-centric process is referred to as software release planning (SRP). This briefing will expose a state of the art on SRP. A survey of the most relevant approaches will be presented. Emphasis will be made on their applicability (concerning e.g. type of development process - being more predictive versus more adaptive, type of system - commercial, open source product or mobile app), tool support and degree of validation in industry. One of these approaches, EVOLVE, will be analysed in detail.Peer ReviewedPostprint (author's final draft

Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly

Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly