Classification of Speech Acts in Public Software Tenders

Abstract

Governments and private sectors currently procure software solutions for industry through public tender using mass distribution websites. This alternative organizes the demand and produces a large number of software tenders. Objective. The present study focuses on analyzing the texts of these documents to characterize them efficiently and explore a particular solution to the general problem known as 'to bid or not to bid.' The tool is based on the automatic classification of speech acts, from where we generate different metrics from the Public Call Software Tender (PCST). Methodology. Our first approach was to use some analysis techniques suggested for Requirements Specifications. In particular, our interest focused on speech acts and the ontology-based on speech acts for analyzing requirements. These works focus on classifying software requirements in the early stages of the life cycle, which gave us a starting point for our work in PCST. We use our tool to analyze a set of four PCSTs downloaded from the Chilean Government's public purchases website for the validation stage. The automatic analysis consisted in categorizing and classifying the four PCST downloaded, obtaining the measured values of the variables used by the metrics. Results. An initial assessment shows that the results of this application agree with the proposals generated manually by expert analysts. Our proposal saves time and effort when looking for relevant tenders. Conclusion. We consider the theory of speech acts, which allows texts to be categorized from a pragmatic point of view. We propose a first version of an automatic text classifier based on characterizing speech acts accompanied by metrics. This tool will allow potential tenderers in a public call for software tenders to decide whether it is worth tendering for the call. Based on these assumptions, we propose to use the identification of speech acts in requirements specifications to calculate a set of metrics that will enable us not only to describe PCST but also to compare them.