Exploring the benefits of utilizing conceptual information in test-to-code traceability

Striving for reliability of software systems often results in immense numbers of tests. Due to the lack of a generally used annotation, finding the parts of code these tests were meant to assess can be a demanding task. This is a valid problem of software engineering called test-to-code traceability...

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Vydané v:2018 IEEE ACM 6th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE) s. 8 - 14
Hlavní autori: Kicsi, András, Tóth, László, Vidács, László
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: New York, NY, USA ACM 28.05.2018
Edícia:ACM Conferences
Predmet:
Artificial intelligence
Computing methodologies > Artificial intelligence > Natural language processing
Data mining
Information retrieval
Large scale integration
LSI
Natural language processing
natural language processing (nlp)
Production
Software and its engineering > Software creation and management > Software verification and validation > Process validation > Traceability
Software engineering
testing
traceability
LSI
natural language processing (nlp)
traceability
testing
ISBN:1450357237, 9781450357234
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Shrnutí:Striving for reliability of software systems often results in immense numbers of tests. Due to the lack of a generally used annotation, finding the parts of code these tests were meant to assess can be a demanding task. This is a valid problem of software engineering called test-to-code traceability. Recent research on the subject has attempted to cope with this problem applying various approaches and their combinations, achieving profound results. These approaches have involved the use of naming conventions during development processes and also have utilized various information retrieval (IR) methods often referred to as conceptual information. In this work we investigate the benefits of textual information located in software code and its value for aiding traceability. We evaluated the capabilities of the natural language processing technique called Latent Semantic Indexing (LSI) in the view of the results of the naming conventions technique on five real, medium sized software systems. Although LSI is already used for this purpose, we extend the viewpoint of one-to-one traceability approach to the more versatile view of LSI as a recommendation system. We found that considering the top 5 elements in the ranked list increases the results by 30% on average and makes LSI a viable alternative in projects where naming conventions are not followed systematically.
ISBN:1450357237
9781450357234
DOI:10.1145/3194104.3194106