Information retrieval based feature analysis for product line adoption in 4GL systems

Abstract

New customers often require custom features of a successfully marketed product. As the number of variants grow, new challenges arise in the maintenance and evolution activities. Software product line (SPL) architecture is a timely answer to these challenges. The SPL adoption however is a large one time investment that affects both technical and organizational issues. From the program code point of view, the extractive approach is appropriate when there are already several product variants. Analyzing the feature structure, the differences and commonalities of the variants lead to the new common architecture. In this work in progress paper we report initial experiments of feature extraction from a set of product variants written in the Magic fourth generation language (4GL). Since existing approaches are mostly designed for mainstream languages, we adapted and reused reverse engineering approaches to the 4GL environment. We followed a semi-automatic feature extraction method, where the higher level features are provided by domain experts. These features are then linked to the internal structure of Magic applications using a textual similarity (IR-based) method. We demonstrate the feasibility of 4GL feature extraction method and validate it on two variants of a real life logistical system each consisting of more than 2000 Magic programs.

Evaluation of Textual Similarity Techniques in Code Level Traceability

“…LSI has been applied for recovering traceability links between various software artifacts, even in feature extraction experiments [16]. LSI is already known to be capable of producing good quality results combined with structural information [17,14].…”

Exploration and Mining of Source Code Level Traceability Links on Stack Overflow

“…LSI (Deerwester et al, 1990) is an older technique which has been used as mainstream in many tasks of semantic analysis. It is often considered one of the base techniques of many software engineering research approaches that rely on information retrieval and it is not new in traceability either (Rompaey and Demeyer, 2009) (Kicsi et al, 2017) and even in testto-code traceability it is used as part of the recent state-of-the-art techniques (Qusef et al, 2011). Similarly to Doc2Vec, it relies on semantic information of text handled as vectors and produces a more compact form of vectors that results in the semantically more similar documents obtaining more similar vectors.…”

Supporting Product Line Adoption by Combining Syntactic and Textual Feature Extraction

“…Magic program analysis tool support is not comparable to mainstream languages, hence this is a research-intensive project. In our current work we used our previously available tools for static analysis of Magic applications and IR-based feature extraction [11].…”

“…In previous work [11], we presented our information retrieval approach to feature extraction similar to our textual analysis, but that information alone proved to be very noisy and incomplete. We not only combine conceptual information with structural one, but present an ecient method for ltering the data as well.…”