Meghan Revelle

Feature location in source code: a taxonomy and survey

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

Using Latent Dirichlet Allocation for automatic categorization of software

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.

Using Data Fusion and Web Mining to Support Feature Location in Software

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%.

Combining textual and structural analysis of software artifacts for traceability link recovery

Existing methods for recovering traceability links among software documentation artifacts analyze textual similarities among these artifacts. It may be the case, however, that related documentation elements share little terminology or phrasing. This paper presents a technique for indirectly recovering these traceability links in requirements documentation by combining textual with structural information as we conjecture that related requirements share related source code elements. A preliminary case study indicates that our combined approach improves the precision and recall of recovering relevant links among documents as compared to stand-alone methods based solely on analyzing textual similarities.

Integrating information retrieval, execution and link analysis algorithms to improve feature location in software

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 or link analyses algorithms 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 on three Java systems indicate that the new feature location techniques based on web mining improve the effectiveness of existing approaches by as much as 87%.

FLAT 3 : feature location and textual tracing tool

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.

An exploratory study on assessing feature location techniques

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 features 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.

Using structural and textual information to capture feature coupling in object-oriented software

Previous studies have demonstrated the relationship between coupling and external software quality attributes, such as fault-proneness, and the application of coupling to software maintenance tasks, such as impact analysis. These previous studies concentrate on class coupling. However, there is a growing focus on the study of features in software, and features are often implemented across multiple classes, meaning class-level coupling measures are not applicable. We ask the pertinent question, “Is measuring coupling at the feature-level also useful?” We define new feature coupling metrics based on structural and textual source code information and extend the unified framework for coupling measurement to include these new metrics. We also conduct three extensive case studies to evaluate these new metrics and answer this research question. The first study examines the relationship between feature coupling and fault-proneness, the second assesses feature coupling in the context of impact analysis, and the third study surveys developers to determine if the metrics align with what they consider to be coupled features. All three studies provide evidence that feature coupling metrics are indeed useful new measures that capture coupling at a higher level of abstraction than classes and can be useful for finding bugs, guiding testing effort, and assessing change impact.

Understanding concerns in software: insights gained from two case studies

Much of the complexity of software arises from the interactions between disparate concerns. Even in well-designed software, some concerns can not always be encapsulated in a module. Research on separation of concerns seeks to address this problem, but we lack an understanding of how programmers conceptualize the notion of a concern and then identify that concern in code. In this work, we have conducted two exploratory case studies to better understand these issues. The case studies involved programmers identifying concerns and associated code in existing, unfamiliar software: GNUs sort.c and the game Minesweeper. Based on our experiences with these two case studies, we have identified several types of concerns and have detailed a number of factors that impact programmer identification of concerns. Based on these insights, we have created two sets of guidelines: one to help programmers identify relevant concerns and another to help programmers identify code relating to concerns.

Spotlight: A Prototype Tool for Software Plans

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.