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Introduction − Making effort estimation as accurate and suitable for software development projects becomes a fundamental stage to favor its success, which is a difficult task, since the application of these techniques in constant changing agile development projects raises the need to evaluate different methods frequently. Objectives− The objective of this study is to provide a state of the art on techniques of effort estimation in agile software development (ASD), performance evaluation and the drawbacks that arise in its application. Method− A systematic mapping was developed involving the creation of research questions to provide a layout of this study, analysis of related words for the implementation of a search query to obtain related studies, application of exclusion, inclusion, and quality criteria to filter nonrelated studies and finally the organization and extraction of the necessary information from each study. Results− 25 studies were selected; the main findings are: the most applied estimation techniques in agile contexts are: Estimation of Story Points (SP) followed by Planning Poker (PP) and Expert Judgment (EJ). The most frequent solutions supported in computational techniques such as: Naive Bayes, Regression Algorithms and Hybrid System; also, the performance evaluation measures Mean Magnitude of Relative Error (MMRE), Prediction Assessment (PRED) and Mean Absolute Error (MAE) have been found to be the most commonly used. Additionally, parameters such as feasibility, experience, and the delivery of expert knowledge, as well as the constant particularity and lack of data in the process of creating models to be applied to a limited number of environments are the challenges that arise the most when estimating software in agile software development (ASD) Conclusions− It has been found there is an increase in the number of articles that address effort estimation in agile development, however, it becomes evident the need to improve the accuracy of the estimation by using estimation techniques supported in machine learning that have been shown to facilitate and improve the performance of this. Key Words − Effort Estimation; Agile Software Development; Issues and Challenges; Automatic Learning; Performance Metrics