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.

Feature location is the process of finding the source code that implements a functional requirement of a software system. It plays an important role in software maintenance activities, but when it is performed manually, it can be challenging and time-consuming, especially for large, long-lived systems. This paper describes a tool called FLAT3 that integrates textual and dynamic feature location techniques along with feature annotation capabilities and a useful visualization technique, providing a complete suite of tools that allows developers to quickly and easily locate the code that implements a feature and then save these annotations for future use.

Data fusion is the process of integrating multiple sources of information such that their combination yields better results than if the data sources are used individually. This paper applies the idea of data fusion to feature location, the process of identifying the source code that implements specific functionality in software. A data fusion model for feature location is presented which defines new feature location techniques based on combining information from textual, dynamic, and web mining analyses applied to software. A novel contribution of the proposed model is the use of advanced web mining algorithms to analyze execution information during feature location. The results of an extensive evaluation indicate that the new feature location techniques based on web mining improve the effectiveness of existing approaches by as much as 62%.

This paper presents an exploratory study of ten feature location techniques that use various combinations of textual, dynamic, and static analyses. Unlike previous studies, the approaches are evaluated in terms of finding multiple relevant methods, not just a single starting point of a feature's implementation. Additionally, a new way of applying textual analysis is introduced by which queries are automatically composed of the identifiers of a method known to be relevant to a feature. Our results show that this new type of query is just as effective as a query formulated by a human. We also provide insights into situations when certain feature location approaches work well and fall short. Our results and observations can be used to guide future research on feature location.

In this paper, we propose a technique called LACT for automatically categorizing software systems in open-source repositories. LACT is based on latent Dirichlet Allocation, an information retrieval method which is used to index and analyze source code documents as mixtures of probabilistic topics. For an initial evaluation, we performed two studies. In the first study, LACT was compared against an existing tool, MUDABlue, for classifying 41 software systems written in C into problem domain categories. The results indicate that LACT can automatically produce meaningful category names and yield classification results comparable to MUDABlue. In the second study, we applied LACT to 43 software systems written in different programming languages such as C/C++, Java, C#, PHP, and Perl. The results indicate that LACT can be used effectively for the automatic categorization of software systems regardless of the underlying programming language or paradigm. Moreover, both studies indicate that LACT can identify several new categories that are based on libraries, architectures, or programming languages, which is a promising improvement as compared to manual categorization and existing techniques.

Software evolution is made difficult by the need to integrate new features with all previously implemented features in the system. We present Spotlight, a prototype editor for software plans that seeks to address this problem by providing the programmer a principled way to separately develop and incrementally integrate independent features.