Improving feature location using structural similarity and iterative graph mapping

Abstract

Abstract. Locating program element(s) relevant to a particular feature is an important step in efficient maintenance of a software system. The existing feature location techniques analyze each feature independently and perform a one-time analysis after being provided an initial input. As a result, these techniques are sensitive to the quality of the input. In this paper, we propose to address the above issues in feature location using an iterative context-aware approach. The underlying intuition is that features are not independent of each other, and the structure of source code resembles the structure of features. The distinguishing characteristics of the proposed approach are: (1) it takes into account the structural similarity between a feature and a program element to determine feature-element relevance; (2) it employs an iterative process to propagate the relevance of the established mappings between a feature and a program element to the neighboring features and program elements. We evaluate our approach using two different systems, DirectBank, a small-scale industry financial system, and Linux kernel, a large-scale open-source operating system. Our evaluation suggests that the proposed approach is more robust and can significantly increase the recall of feature location with only a minor decrease of precision.

Evaluation of Test Case Generation based on a Software Product Line for Model Transformation

“…For instance, an SPL with n features can yield up to 2n individual systems (or products) (Classen et al, 2011). Examples of bigger SPLs include: Linux 2.6.32.2 kernel, with 6052 features (Peng et al, 2013) and Eclipse SPL, 1024 features (Johansen et al, 2012). Fig.…”

A large scale Linux-Kernel based benchmark for feature location research

“…We propose to overcome the above limitation by an iterative context-aware feature location technique [5]. This technique integrates IR technique with structural similarity and graph matching.…”

“…To date, we have conducted a few studies using this benchmark, including a large-scale comparative study of the effectiveness of 10 IR techniques for feature location [10], and two new approaches to feature location [5] [11]. In addition to our own research, we believe this benchmark will also have a strong positive effect on the research community, because it will bring opportunities of communication and collaboration among different researchers, leading to clear research goal and development of feature location methods.…”

Clustering Classes in Packages for Program Comprehension

“…Program comprehension is one of the most frequently performed activities in software maintenance [1,2]. It is a process whereby a software practitioner understands a program using both knowledge of the domain and semantic and syntax knowledge, to build a mental model of the program [3,4].…”