SUMMARY
Feature location is the activity of identifying an initial location in the source code that implements functionality in a software system. Many feature location techniques have been introduced that automate some or all of this process, and a comprehensive overview of this large body of work would be beneficial to researchers and practitioners. This paper presents a systematic literature survey of feature location techniques. Eighty-nine articles from 25 venues have been reviewed and classified within the taxonomy in order to organize and structure existing work in the field of feature location. The paper also discusses open issues and defines future directions in the field of feature location. Copyright © 2011 John Wiley & Sons, Ltd.

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Feature location in source code: a taxonomy and survey

This paper recasts the problem of feature location in source code as a decision-making problem in the presence of uncertainty. The solution to the problem is formulated as a combination of the opinions of different experts. The experts in this work are two existing techniques for feature location: a scenario-based probabilistic ranking of events and an information-retrieval-based technique that uses latent semantic indexing. The combination of these two experts is empirically evaluated through several case studies, which use the source code of the Mozilla Web browser and the Eclipse integrated development environment. The results show that the combination of experts significantly improves the effectiveness of feature location as compared to each of the experts used independently

Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval

The paper addresses the problem of concept location in source code by presenting an approach which combines formal concept analysis (FCA) and latent semantic indexing (LSI). In the proposed approach, LSI is used to map the concepts expressed in queries written by the programmer to relevant parts of the source code, presented as a ranked list of search results. Given the ranked list of source code elements, our approach selects most relevant attributes from these documents and organizes the results in a concept lattice, generated via FCA. The approach is evaluated in a case study on concept location in the source code of eclipse, an industrial size integrated development environment. The results of the case study show that the proposed approach is effective in organizing different concepts and their relationships present in the subset of the search results. The proposed concept location method outperforms the simple ranking of the search results, reducing the programmers' effort.

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Combining Formal Concept Analysis with Information Retrieval for Concept Location in Source Code

Most current software systems contain undocumented high-level ideas implemented across multiple files and modules. When developers perform program maintenance tasks, they often waste time and effort locating and understanding these scattered concerns. We have developed a semi-automated concern location and comprehension tool, Find-Concept, designed to reduce the time developers spend on maintenance tasks and to increase their confidence in the results of these tasks. Find-Concept is effective because it searches a unique natural language-based representation of source code, uses novel techniques to expand initial queries into more effective queries, and displays search results in an easy-to-comprehend format. We describe the Find-Concept tool, the underlying program analysis, and an experimental study comparing Find-Concept's search effectiveness with two state-of-the-art lexical and information retrieval-based search tools. Across nine action-oriented concern location tasks derived from open source bug reports, our Eclipse-based tool produced more effective queries more consistently than either competing search tool with similar user effort.

Using natural language program analysis to locate and understand action-oriented concerns

As software systems continue to grow and evolve, locating code for maintenance and reuse tasks becomes increasingly difficult. Existing static code search techniques using natural language queries provide little support to help developers determine whether search results are relevant, and few recommend alternative words to help developers reformulate poor queries. In this paper, we present a novel approach that automatically extracts natural language phrases from source code identifiers and categorizes the phrases and search results in a hierarchy. Our contextual search approach allows developers to explore the word usage in a piece of software, helping them to quickly identify relevant program elements for investigation or to quickly recognize alternative words for query reformulation. An empirical evaluation of 22 developers reveals that our contextual search approach significantly outperforms the most closely related technique in terms of effort and effectiveness.

Automatically capturing source code context of NL-queries for software maintenance and reuse

Concept location in source code is the process that identifies where a software system implements a specific concept. While it is well accepted that concept location is essential for the maintenance of complex procedural code like code written in C, it is much less obvious whether it is also needed for the maintenance of the object-oriented code. After all, the object-oriented code is structured into classes and well-designed classes already implement concepts, so the issue seems to be reduced to the selection of the appropriate class. The objective of our work is to see if the techniques for concept location are still needed (they are) and whether object-oriented structuring facilitates concept location (it does not). This paper focuses on static concept location techniques that share common prerequisites and are search the source code using regular expression matching, or static program dependencies, or information retrieval. The paper analyses these techniques to see how they compare to each other in terms of their respective strengths and weaknesses.

Static techniques for concept location in object-oriented code

Incremental software change adds new functionality to software. It is the foundation of software evolution, maintenance, iterative development, agile development, and other software processes. Highly interactive tool JRipples provides the programmer with the organizational support that makes the incremental change process easier and more systematic. JRipples supports impact analysis and change propagation, the two most difficult activities of the incremental change.

JRipples: a tool for program comprehension during incremental change

Impact analysis is a specialized process of program comprehension that investigates the nature and extent of a planned software change. Traditionally, impact analysis involves inspecting dependencies among the software components of a fixed granularity; these components constitute a dependency graph. In this paper, we argue that a single granularity is insufficient and leads to imprecise analysis. We explain how the precision can be improved by variable granularity, where the programmers choose among the granularity of classes, the granularity of class members, and the granularity of code fragments. We assess the resulting precision in a case study on open-source software.

Variable granularity for improving precision of impact analysis

Partial comprehension is a necessity in the evolution and maintenance of very large software systems. The programmers form not only partial comprehension of the code, but also partial comprehension of the application domain. To describe the comprehension process, we introduce ontology fragments and investigate how programmers form, use and extend them before and during concept location; concept location is a prerequisite of code changes. We conducted case studies of concept location in two large systems, Eclipse and Mozilla, that both have more than 50,000 methods. Using grep search and ontology fragments, the programmers were able to locate the concepts after inspecting on average less than 10 methods and operating with ontology fragments of around 14 concepts, a very small fraction of the total.

Partial Domain Comprehension in Software Evolution and Maintenance

The paper presents a new approach to source code exploration, which is the result of integrating the Google Desktop Search (GDS) engine into the Eclipse development environment. The resulting search engine, named Google Eclipse Search (GES), provides improved searching in Eclipse software projects. The paper advocates for a component-based approach that allows us to develop strong tools, which support various maintenance tasks, by leveraging the strengths of existing frameworks and components. The development effort for such tools is reduced, while customization and flexibility, to fully support user needs, is maintained GES allows developers to search software projects in a manner similar to searching the internet or their own desktops. The proposed approach takes advantages of the power of GDS for quick and accurate searching and of Eclipse?s extensibility. The paper discusses usage scenarios, advantages, limitations, and possible extensions of the proposed tandem.

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M. Petrenko

Papers

Static techniques for concept location in object-oriented code

JRipples: a tool for program comprehension during incremental change

Variable granularity for improving precision of impact analysis

Partial Domain Comprehension in Software Evolution and Maintenance

Source Code Exploration with Google