## 1 Introduction
They also need to continuously evolve the product by innovating the value proposition to maintain a competitive advantage because of continuously changing customer needs, competitor pressure and technological changes. The number of interactions with the customers will bring a large amount of data (requirement gathering) which must be analysed (requirement analysis), prioritizing the features forthe next release (requirement prioritization, initially for MVP and later for the product), documented (requirement documentation), requirement validation (for instance using prototypes) and product validation (for instance, using MVP). Product validations bring useful insights that lead to a new phase of requirement engineering to evolve the product. These requirement engineering activities become more structured as the product captures market and the startup gains growth. The evolutionary requirement engineering process model helps startups to continuously innovate their value proposition to maintain and enhance their market share. In other words, the software should always solve the ever-changing needs of the customers and provide them benefits that exceed their expectations in a cost effective way (to enhance perceived customer value). Otherwise, delivering the product for which there is no market (product provides a solution for which there are no customer needs) leads to startup failures. The literature does not report startup failures cases except to state that high failure rates among software startups are at least partly attributable to software engineering issues in general and requirement engineering in particular (like the inability to find customers due to a poor product/market fit, etc.) [1; 2; 3; 4; 5]. This means that the chances of achieving a good product/market fit and hence enough growth increase if the requirement engineering is carried out effectively. To enhance the startup success rates by focusing on product/market fit issue, it is essential that the startup team should get the following regarding requirement engineering:  
1. The practices undertaken in big companies that are reported in the context of the startups in the literature that provides lessons and guidelines to the other startups that they could customize as per their unique context. 2. The experience reports of the software practitioners that reflect their experiences in the software engineering domain related to enhancing the success rates of startups. 3. The evaluation studies (including empirical studies and those solutions that are implemented in real settings) in the startup context that will help other startups to reproduce the reported empirical studies and execute the reported research solutions in their context. 4. Validation studies (solutions that are validated in laboratory settings) to help startups modify these solutions and execute them in their specific contexts. Startups have their own context. The studies reported in the literature which are based generic startup contexts or specific to few startups, must be customized by the startups that find them adoptable in their context. The limited number of studies in the literature limits their streamlined adoption by startups as increases in the number of studies leads to wider options, access to quality metrics (like citations), availability of extension of studies, availability of experimental evaluations of studies, etc. In particular, it provides startups with access to a range of studies which are more structured, better evolved, better evaluated and mature compared with the limited literature. The objective of this mapping study is to study the state of research in the requirement engineering activity in the startup context by identifying the research trends in terms of focused activities, research type, publication evolution relative to time and underlying research motivation.