Can We Predict Dependencies Using Domain information?

2013 35th International Conference on Software Engineering (ICSE)

Roy

et al. 2013

Self Cite

Abstract-Knowledge of similar code fragments, also known as code clones, is important to many software maintenance activities including bug fixing, refactoring, impact analysis and program comprehension. While a great deal of research has been conducted for finding techniques and implementing tools to identify code clones, little research has been done to analyze the relationships between code clones and other aspects of software. In this paper, we attempt to uncover the relationships between code clones and coupling among domain-level components. We report on a case study of a large-scale open source enterprise system, where we demonstrate that the probability of finding code clones among components with domain-based coupling is more than 90%. While such a probabilistic view does not replace a clone detection tool per se, it certainly has the potential to complement the existing tools by providing the probability of having code clones between software components. For example, it can both reduce the clone search space and provide a flexible and language independent way of focusing only on a specific part of the system. It can also provide a higher level of abstraction to look at the cloning relationships among software components.

“…Our domain analysis model is derived from the models proposed by Aryani et al [3], [1], [2]. The three key elements at the domain level are modelled as follows:…”

Section: A Notation and Definitionsmentioning

confidence: 99%

Section: Threats To Validitymentioning

confidence: 99%

See 1 more Smart Citation

On the relationships between domain-based coupling and code clones: An exploratory study

Rahman

2013 35th International Conference on Software Engineering (ICSE)

Roy

et al. 2013

Self Cite

“…Mapping domain entities to source code has been used for concept location [26], [27], [25], developing reverse engineering tools [28], [29], and supporting software change process [30]. In a recent work, a coupling metric, termed as domain-based coupling, has been proposed as a method of approximation for software dependencies using solely domain information [31]. We build our approach based on the hypothesis that this metric and the conceptual coupling can complement each other and combining them improves the accuracy of predicting software dependencies.…”

Section: B Domain-based Couplingmentioning

confidence: 99%

“…Conceptual couplings are independent from the underlying programming language or programming paradigm. Likewise, domain-based couplings are derived from the domainlevel relationships embedded in software components, which are independent from software implementation [21]. As such, domain-based coupling can be used to detect dependencies even without access to the source code or the database.…”

Section: Introductionmentioning

confidence: 99%

Combining Conceptual and Domain-Based Couplings to Detect Database and Code Dependencies

Gethers

2012 IEEE 12th International Working Conference on Source Code Analysis and Manipulation

Poshyvanyk

2012

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Abstract-Knowledge of software dependencies plays an important role in program comprehension and other maintenance activities. Traditionally, dependencies are derived by source code analysis; however, such an approach can be difficult to use in multi-tier hybrid software systems, or legacy applications where conventional code analysis tools simply do not work as is. In this paper, we propose a hybrid approach to detecting software dependencies by combining conceptual and domainbased coupling metrics. In recent years, a great deal of research focused on deriving various coupling metrics from these sources of information with the aim of assisting software maintainers. Conceptual metrics specify underlying relationships encoded by developers in identifiers and comments of source code classes whereas domain metrics exploit coupling manifested in domainlevel information of software components and it is independent from software implementation. The proposed approach is independent from programming language, as such it can be used in multi-tier hybrid systems or legacy applications. We report the results of an empirical case study on a large-scale enterprise system where we demonstrate that the combined approach is able to detect database and source code dependencies with higher precision and recall as compared to its standalone constituents.

Predicting dependences using domain‐based coupling

J Software Evolu Process

Perin

Lungu

et al. 2013

Self Cite

Software dependences play a vital role in programme comprehension, change impact analysis and other software maintenance activities. Traditionally, these activities are supported by source code analysis; however, the source code is sometimes inaccessible or difficult to analyse, as in hybrid systems composed of source code in multiple languages using various paradigms (e.g. object-oriented programming and relational databases). Moreover, not all stakeholders have adequate knowledge to perform such analyses. For example, non-technical domain experts and consultants raise most maintenance requests; however, they cannot predict the cost and impact of the requested changes without the support of the developers. We propose a novel approach to predicting software dependences by exploiting the coupling present in domain-level information. Our approach is independent of the software implementation; hence, it can be used to approximate architectural dependences without access to the source code or the database. As such, it can be applied to hybrid systems with heterogeneous source code or legacy systems with missing source code. In addition, this approach is based solely on information visible and understandable to domain users; therefore, it can be efficiently used by domain experts without the support of software developers. We evaluate our approach with a case study on a large-scale enterprise system, in which we demonstrate how up to 65% of the source code dependences and 77% of the database dependences are predicted solely based on domain information. Definition 6Given two tables t, t 0 2 TBL, we say that t has a direct relation to t 0 if and only if t. FK. t 0 . Definition 7Given two tables t, t 0 2 TBL, we say that t has indirect relation to t 0 if and only if t. FK. FK À 1 . t 0 . 58 AMIR ARYANI ET AL.We use the well-known definitions of precision (P q ) and recall (R) to evaluate the outcomes of a given query: 60 AMIR ARYANI ET AL.• What kinds of software changes and maintenance activities can benefit the most from the proposed approach? We envisage that the main application of the proposed approach will be estimating the change propagation prior to maintenance activities such as bug fixes and software enhancements. Lientz and Swanson [24] classified software changes as perfective, adaptive, corrective and 70 AMIR ARYANI ET AL.