Abstract
Software Requirements Specification (SRS) is considered a highly critical artifact in the software development. All phases in software development are influenced by this artifact. Defects in software requirements may higher the risk of project overschedule that contributes to cost overrun of the project.Researchers have shown that finding defects in the initial software development phase is important becausethe cost of the bug is cheaper if it is fixed early. Hence, our main goal is to provide a platform for requirement engineers to produce better requirement specifications. We propose AmbiDetect, a (prototype) tool toautomatically classify ambiguous software requirements. AmbiDetect combines text mining and machine learning for ambiguous requirement specification detection. The text mining technique is used to extract classification features as well as generating the training set.AmbiDetect usesa machine learning technique to perform the ambiguous requirement specification detection. From an initial user study to validate the tool, the result indicates that the accuracy of detection is reasonably acceptable.Although AmbiDetect is an early experimental tool, we optimist that this tool can be a platform to improve SRS quality.
Highlights
Software requirements specification (SRS) is the foundation of software and considered a highly critical document in software development
It is similar to the term “Garbage in, Garbage out” that has been commonly used in computer programming which denotes “If there is a logical error in software, or incorrect data are entered, the result will probably be either a wrong answer or a system crash”, Dictionary.com, 2018
The tool capable to perform ambiguity detection only for SRS that is written in Malay because the generated dataset is originated from Malaysian industrial software developments which are only written in Malay
Summary
Software requirements specification (SRS) is the foundation of software and considered a highly critical document in software development. Our main goal is to provide a platform for requirement engineers to produce better SRSs. As an initial step, we propose AmbiDetect, a (prototype) tool to automatically detect ambiguous software requirements specification. We apply the ambiguous SRS detection proposed by Osman and Zaharin (2018) for developing the classification model To this end, the tool capable to perform ambiguity detection only for SRS that is written in Malay (language) because the generated dataset is originated from Malaysian industrial software developments which are only written in Malay. The tool listed all the feature-words that are used for machine learning classification (Figure 4) In this way, the user may exclude the words that are not influential in classifying the requirements. This tool aims at providing the information of ambiguous and unambiguous requirements based on the classification model. This task was conducted manually since the Malay language currently does not have a library or tool to perform stop word removal and stemming
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