Software Evolution Understanding: Automatic Extraction of Software Identifiers Map for Object-Oriented Software Systems

Software companies usually develop a set of product variants within the same family that share certain functions and differ in others. Variations across software variants occur to meet different customer requirements. Thus, software product variants evolve overtime to cope with new requirements. A software engineer who deals with this family may find it difficult to understand the evolution scenarios that have taken place over time. In addition, software identifier names are important resources to understand the evolution scenarios in this family. This paper introduces an automatic approach called Juana’s approach to detect the evolution scenario across two product variants at the source code level and identifies the common and unique software identifier names across software variants source code. Juana’s approach refers to common and unique identifier names as a software identifiers map and computes it by comparing software variants to each other. Juana considers all software identifier names such as package, class, attribute, and method. The novelty of this approach is that it exploits common and unique identifier names across the source code of software variants, to understand the evolution scenarios across software family in an efficient way. For validity, Juana was applied on ArgoUML and Mobile Media software variants. The results of this evaluation validate the relevance and the performance of the approach as all evolution scenarios were correctly detected via a software identifiers map

Naming the Identified Feature Implementation Blocks from Software Source Code

Identifying software identifiers that implement a particular feature of a software product is known as feature identification. Feature identification is one of the most critical and popular processes performed by software engineers during software maintenance activity. However, a meaningful name must be assigned to the Identified Feature Implementation Block (IFIB) to complete the feature identification process. The feature naming process remains a challenging task, where the majority of existing approaches manually assign the name of the IFIB. In this paper, the approach called FeatureClouds was proposed, which can be exploited by software developers to name the IFIBs from software code. FeatureClouds approach incorporates word clouds visualization technique to name Feature Blocks (FBs) by using the most frequent words across these blocks. FeatureClouds had evaluated by assessing its added benefit to the current approaches in the literature, where limited tool support was supplied to software developers to distinguish feature names of the IFIBs. For validity, FeatureClouds had applied to draw shapes and ArgoUML software. The findings showed that the proposed approach achieved promising results according to well-known metrics in terms of Precision and Recall

Mining Features from the Object-Oriented Source Code of a Collection of Software Variants Using Formal Concept Analysis and Latent Semantic Indexing

International audienceCompanies often develop a set of software variants that share some features and differ in other ones to meet specific requirements. To exploit existing software variants and build a software product line (SPL), a feature model of this SPL must be built as a first step. To do so, it is necessary to mine optional and mandatory features from the source code of the software variants. Thus, we propose, in this paper, a new approach to mine features from the object-oriented source code of a set of software variants based on Formal Concept Analysis and Latent Semantic Indexing. To validate our approach, we applied it on ArgoUML and Mobile Media case studies. The results of this evaluation validate the relevance and the performance of our proposal as most of the features were correctly identified

Maßgeschneiderte Produktlinienextraktion

Industry faces an increasing number of challenges regarding the functionality, efficiency and reliability of software. A common approach to reduce the linked development effort and respective costs are model-based languages, such as Matlab/Simulink and statecharts. While these languages help companies during development of single systems, the high demand for customized software is an increasing challenge. As a result, variants with high similarity and only slight differences have to be developed in an efficient way. As reimplementation of complex functionality for each variant is no option, copies of existing solutions are often modified for new customers. In the short-run, this so-called clone-and-own approach allows to save costs as existing solutions can easily be reused. However, this approach also involves risks as the relations between the copied systems are rarely documented and errors have to be fixed for each variant in isolation. Thus, with a growing number of potentially large system copies, the resulting maintenance effort can become a problem. To overcome these problems, this thesis contributes an approach to semi-automatically migrate existing model variants to software product lines. These product lines allow to generate all variants from the identified reusable artifacts. As industry uses a variety of different modeling languages, the focus of the approach lies on an easy adaptation for different languages. Furthermore, the approach can be custom-tailored to include domain knowledge or language-specific details in the variability identification. The first step of the approach performs a high-level analysis of variants to identify outliers (e.g., variants that diverged too much from the rest) and clusters of strongly related variants. The second step executes variability mining to identify corresponding low-level variability relations (i.e. the common and varying parts) for these clusters. The third step uses these detailed variability relations for an automatic migration of the compared variants to a delta-oriented software product line. The approach is evaluated using publicly available case studies with industrial background as well as model variants provided by an industry partner.Die Industrie steht einer steigenden Anzahl an Herausforderungen bezüglich der Funktionalität, Effizienz und Zuverlässigkeit von Software gegenüber. Um den damit verbundenen Entwicklungsaufwand und entsprechende Kosten zu reduzieren, werden häufig modellbasierte Sprachen wie Matlab/Simulink oder Zustandsautomaten eingesetzt. Obwohl diese Sprachen die Unternehmen während der Entwicklung von Einzelsystemen unterstützen, führt die große Nachfrage nach maßgeschneiderter Software zu neuen Herausforderungen. Entsprechend müssen Varianten mit hoher Ähnlichkeit und nur geringfügigen Unterschieden effizient entwickelt werden. Da eine Neuimplementierung komplexer Funktionalität für jede Variante keine Option darstellt, werden häufig Kopien existierender Lösungen für Kunden angepasst. Auf kurze Sicht ermöglicht dieser sogenannte clone-and-own-Ansatz Kosten zu sparen, da existierende Lösungen leicht wiederverwendet werden können. Jedoch birgt der Ansatz auch Risiken, da Beziehungen zwischen den Systemkopien selten dokumentiert werden und Fehler für jede der Variante einzeln behoben werden müssen. Somit kann mit einer wachsenden Anzahl an möglicherweise umfangreichen Systemkopien der Wartungsaufwand zu einem Problem werden. Um diese Probleme zu lösen, bietet diese Arbeit einen Ansatz zur semi-automatischen Überführung existierender Modellvarianten in Softwareproduktlinien. Diese ermöglichen eine anschließende Generierung der Varianten aus den identifizierten wiederverwendbaren Artefakten. Da in der Industrie eine große Menge von Modellierungssprachen eingesetzt wird, liegt der Fokus auf der einfachen Adaption für unterschiedliche Sprachen. Zusätzlich kann durch Einbeziehung von Expertenwissen oder sprachspezifische Details die Variabilitätsidentifikation beeinflusst werden. Der erste Schritt des Ansatzes analysiert die Varianten auf hohem Abstraktionslevel, um Außenseiter (z.B. Varianten die stark von den restlichen Variaten abweichen) und Cluster von stark verwandten Varianten zu identifizieren. Der zweite Schritt analysiert diese Cluster auf niedrigem Abstraktionslevel, um entsprechende Variabilitätsrelationen (d.h. gemeinsame und unterschiedliche Teile) zu identifizieren. Der dritte Schritt nutzt diese detaillierten Variabilitätsrelationen für eine automatische Migration der verglichenen Varianten in eine delta-orientierte Softwareproduktlinie. Der Ansatz ist an Fallstudien mit industriellem Kontext sowie Modellvarianten eines Industriepartners evaluiert worden