Method and system for testing software artefacts for reuse via semantic search

A computing apparatus for identifying and recommending software artefacts for reuse for testing new software functionality, the computing apparatus comprising:

• a processor;
• a memory;
• and
• a communication interface coupled to each of the processor and the memory,
• wherein the processor is configured to:
• receive, via the communication interface, a first set of stories, each respective story included in the first set of stories including a natural language textual description of a respective set of user requirements;
• convert, by using a predetermined text vectorization technique, each respective story into a respective embedding that includes a respective vector representation of the respective story;
• store each respective embedding in a vector database within the memory;
• receive, from a first user via the communication interface, a new story that includes a natural language textual description of a first set of requirements that correspond to the first user;
• convert, by using the predetermined text vectorization technique, the new story into a new embedding;
• compare the new embedding to each respective embedding stored in the vector database;
• generate, based on a result of the comparison for each respective embedding, a respective similarity score that relates to a respective semantic similarity between the respective embedding and the new embedding;
• rank-order each respective embedding based on the respective similarity score;
• for a respective embedding having at least a predetermined first rank, identify at least one respective testing artefact that is associated with the respective story;
• generate a list of recommendations based on the identified at least one testing artefact;
• and
• display the list via a display that is accessible by the first user.

A non-transitory computer readable storage medium storing instructions for identifying and recommending software artefacts for reuse for testing new software functionality, the storage medium comprising executable code which, when executed by a processor, causes the processor to:

• receive a first set of stories, each respective story included in the first set of stories including a natural language textual description of a respective set of user requirements;
• convert, by using a predetermined text vectorization technique, each respective story into a respective embedding that includes a respective vector representation of the respective story;
• store each respective embedding in a vector database;
• receive, from a first user, a new story that includes a natural language textual description of a first set of requirements that correspond to the first user;
• convert, by using the predetermined text vectorization technique, the new story into a new embedding;
• compare the new embedding to each respective embedding stored in the vector database;
• generate, based on a result of the comparing for each respective embedding, a respective similarity score that relates to a respective semantic similarity between the respective embedding and the new embedding;
• rank-order each respective embedding based on the respective similarity score;
• for a respective embedding having at least a predetermined first rank, identify at least one respective testing artefact that is associated with the respective story;
• generate a list of recommendations based on the identified at least one testing artefact;
• and
• display the list via a display that is accessible by the first user.