Jenny Morales (Autonomous University of Chile), Cristian Rusu (Pontifical Catholic University of Chile), Federico Botella (Miguel Hernández University of Elche) and Daniela Quiñones (Pontifical Catholic University of Chile).
Abstract. Programmers use various software development artifacts in their work, such as programming environments, design documents, and programming codes. These software artifacts can be studied and improved based on usability and User eXperience (UX) factors. In this paper, we consider programmers to be a specific case of users and analyze different elements that influence their experience in this specific context. We conducted a systematic literature review of papers published over the last ten years related to 1) the definition of the Programmer eXperience (PX); 2) the PX, UX, and usability factors regarding the programming environments, design documents, and programming codes; and 3) sets of heuristics to evaluate the software development artifacts mentioned before. We analyzed 73 articles, and the results obtained show that: 1) the important elements that influence the PX are the motivation of programmers and the choice of tools they use in their work, such as programming environments; 2) most of the identified studies (59%) aimed to evaluate the influence of the PX, UX, and usability on programming environments; 3) the majority of the studies (70%) used methods such as usability tests and/or heuristic evaluation methods; and 4) four sets of heuristics are used to evaluate software development artifacts in relation to programming environments, programming languages, and application programming interfaces. The results suggest that further research in this area is necessary to better understand and evaluate the concept of the PX.
Keywords. Heuristic evaluation; Programmer eXperience; Systematic literature review; User eXperience; Usability
José L. Ruiz (Miguel Hernández University of Elche) and Inmaculada Sirvent(Miguel Hernández University of Elche).
Abstract. Data Envelopment Analysis (DEA) is extended to the evaluation of performance of organizations within the framework of the implementation of plans for improvements that set management goals. Managers usually set goals without having any evidence that they will be achievable at the moment of conduct- ing performance evaluation or, on the contrary, they may set little too unambitious goals. Using DEA for the benchmarking ensures an evaluation in terms of targets that both are attainable and represent best practices. In addition, the approach we propose adjusts the DEA benchmarking to the goals in order to consider the policy of improvements that was pursued with the setting of such goals. From the method- ological point of view, the models that minimize the distance to the DEA strong efficient frontier are extended to incorporate goal information. Specifically, the models developed seek DEA targets that are as close as possible to both actual performances and management goals. To illustrate, we examine an example that is concerned with the evaluation of performance of public Spanish universities.
Keywords. Performance evaluation; Benchmarking; Goals; DEA; Target setting
Lipschitz Modulus of the Optimal Value in Linear Programming (2019). Journal of Optimization Theory and Applications, 182, 133-152.
María Jesús Gisbert (Miguel Hernández University of Elche), María Josefa Cánovas (Miguel Hernández University of Elche), Juan Parra (Miguel Hernández University of Elche) and Fco. Javier Toledo (Miguel Hernández University of Elche).
Abstract. The present paper is devoted to the computation of the Lipschitz modulus of the optimal value function restricted to its domain in linear programming under different types of perturbations. In the first stage, we study separately perturbations of the right-hand side of the constraints and perturbations of the coefficients of the objective function. Secondly, we deal with canonical perturbations, i.e., right-hand side perturbations together with linear perturbations of the objective. We advance that an exact formula for the Lipschitz modulus in the context of right-hand side perturbations is provided, and lower and upper estimates for the corresponding moduli are also established in the other two perturbation frameworks. In both cases, the corresponding upper estimates are shown to provide the exact moduli when the nominal (original) optimal set is bounded. A key strategy here consists in taking advantage of the background on calmness in linear programming and providing the aimed Lipschitz modulus through the computation of a uniform calmness constant.
Keywords. Lipschitz modulus; Optimal value; Linear programming; Variational analysis; Calmness