Malcom Gethers

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

On the Equivalence of Information Retrieval Methods for Automated Traceability Link Recovery

We present an empirical study to statistically analyze the equivalence of several traceability recovery methods based on Information Retrieval (IR) techniques. The analysis is based on Principal Component Analysis and on the analysis of the overlap of the set of candidate links provided by each method. The studied techniques are the Jensen-Shannon (JS) method, Vector Space Model (VSM), Latent Semantic Indexing (LSI), and Latent Dirichlet Allocation (LDA). The results show that while JS, VSM, and LSI are almost equivalent, LDA is able to capture a dimension unique to the set of techniques which we considered.

Integrated impact analysis for managing software changes

The paper presents an adaptive approach to perform impact analysis from a given change request to source code. Given a textual change request (e.g., a bug report), a single snapshot (release) of source code, indexed using Latent Semantic Indexing, is used to estimate the impact set. Should additional contextual information be available, the approach configures the best-fit combination to produce an improved impact set. Contextual information includes the execution trace and an initial source code entity verified for change. Combinations of information retrieval, dynamic analysis, and data mining of past source code commits are considered. The research hypothesis is that these combinations help counter the precision or recall deficit of individual techniques and improve the overall accuracy. The tandem operation of the three techniques sets it apart from other related solutions. Automation along with the effective utilization of two key sources of developer knowledge, which are often overlooked in impact analysis at the change request level, is achieved. To validate our approach, we conducted an empirical evaluation on four open source software systems. A benchmark consisting of a number of maintenance issues, such as feature requests and bug fixes, and their associated source code changes was established by manual examination of these systems and their change history. Our results indicate that there are combinations formed from the augmented developer contextual information that show statistically significant improvement over standalone approaches.

On integrating orthogonal information retrieval methods to improve traceability recovery

Different Information Retrieval (IR) methods have been proposed to recover traceability links among software artifacts. Until now there is no single method that sensibly outperforms the others, however, it has been empirically shown that some methods recover different, yet complementary traceability links. In this paper, we exploit this empirical finding and propose an integrated approach to combine orthogonal IR techniques, which have been statistically shown to produce dissimilar results. Our approach combines the following IR-based methods: Vector Space Model (VSM), probabilistic Jensen and Shannon (JS) model, and Relational Topic Modeling (RTM), which has not been used in the context of traceability link recovery before. The empirical case study conducted on six software systems indicates that the integrated method outperforms stand-alone IR methods as well as any other combination of non-orthogonal methods with a statistically significant margin.

Using Relational Topic Models to capture coupling among classes in object-oriented software systems

Coupling metrics capture the degree of interaction and relationships among source code elements in software systems. A vast majority of existing coupling metrics rely on structural information, which captures interactions such as usage relations between classes and methods or execute after associations. However, these metrics lack the ability to identify conceptual dependencies, which, for instance, specify underlying relationships encoded by developers in identifiers and comments of source code classes. We propose a new coupling metric for object-oriented software systems, namely Relational Topic based Coupling (RTC) of classes, which uses Relational Topic Models (RTM), generative probabilistic model, to capture latent topics in source code classes and relationships among them. A case study on thirteen open source software systems is performed to compare the new measure with existing structural and conceptual coupling metrics. The case study demonstrates that proposed metric not only captures new dimensions of coupling, which are not covered by the existing coupling metrics, but also can be used to effectively support impact analysis.

Blending Conceptual and Evolutionary Couplings to Support Change Impact Analysis in Source Code

The paper presents an approach that combines conceptual and evolutionary techniques to support change impact analysis in source code. Information Retrieval (IR) is used to derive conceptual couplings from the source code in a single version (release) of a software system. Evolutionary couplings are mined from source code commits. The premise is that such combined methods provide improvements to the accuracy of impact sets. A rigorous empirical assessment on the changes of the open source systems Apache httpd, ArgoUML, iBatis, and KOffice is also reported. The results show that a combination of these two techniques, across several cut points, provides statistically significant improvements in accuracy over either of the two techniques used independently. Improvements in recall values of up to 20% over the conceptual technique in KOffice and up to 45% over the evolutionary technique in iBatis were reported.

Methodbook: Recommending Move Method Refactorings via Relational Topic Models

During software maintenance and evolution the internal structure of the software system undergoes continuous changes. These modifications drift the source code away from its original design, thus deteriorating its quality, including cohesion and coupling of classes. Several refactoring methods have been proposed to overcome this problem. In this paper we propose a novel technique to identify Move Method refactoring opportunities and remove the Feature Envy bad smell from source code. Our approach, coined as Methodbook, is based on relational topic models (RTM), a probabilistic technique for representing and modeling topics, documents (in our case methods) and known relationships among these. Methodbook uses RTM to analyze both structural and textual information gleaned from software to better support move method refactoring. We evaluated Methodbook in two case studies. The first study has been executed on six software systems to analyze if the move method operations suggested by Methodbook help to improve the design quality of the systems as captured by quality metrics. The second study has been conducted with eighty developers that evaluated the refactoring recommendations produced by Methodbook. The achieved results indicate that Methodbook provides accurate and meaningful recommendations for move method refactoring operations.

Improving software modularization via automated analysis of latent topics and dependencies

Oftentimes, during software maintenance the original program modularization decays, thus reducing its quality. One of the main reasons for such architectural erosion is suboptimal placement of source-code classes in software packages. To alleviate this issue, we propose an automated approach to help developers improve the quality of software modularization. Our approach analyzes underlying latent topics in source code as well as structural dependencies to recommend (and explain) refactoring operations aiming at moving a class to a more suitable package. The topics are acquired via Relational Topic Models (RTM), a probabilistic topic modeling technique. The resulting tool, coined as R3 (Rational Refactoring via RTM), has been evaluated in two empirical studies. The results of the first study conducted on nine software systems indicate that R3 provides a coupling reduction from 10% to 30% among the software modules. The second study with 62 developers confirms that R3 is able to provide meaningful recommendations (and explanations) for move class refactoring. Specifically, more than 70% of the recommendations were considered meaningful from a functional point of view.

Triaging incoming change requests: Bug or commit history, or code authorship?

There is a tremendous wealth of code authorship information available in source code. Motivated with the presence of this information, in a number of open source projects, an approach to recommend expert developers to assist with a software change request (e.g., a bug fixes or feature) is presented. It employs a combination of an information retrieval technique and processing of the source code authorship information. The relevant source code files to the textual description of a change request are first located. The authors listed in the header comments in these files are then analyzed to arrive at a ranked list of the most suitable developers. The approach fundamentally differs from its previously reported counterparts, as it does not require software repository mining. Neither does it require training from past bugs/issues, which is often done with sophisticated techniques such as machine learning, nor mining of source code repositories, i.e., commits. An empirical study to evaluate the effectiveness of the approach on three open source systems, ArgoUML, JEdit, and MuCommander, is reported. Our approach is compared with two representative approaches: (1) using machine learning on past bug reports, and (2) based on commit logs. The presented approach is found to provide recommendation accuracies that are equivalent or better than the two compared approaches. These findings are encouraging, as it opens up a promising and orthogonal possibility of recommending developers without the need of any historical change information.

Assigning change requests to software developers

The paper presents an approach to recommend a ranked list of expert developers to assist in the implementation of software change requests (e.g., bug reports and feature requests). An Information Retrieval (IR)‐based concept location technique is first used to locate source code entities, e.g., files and classes, relevant to a given textual description of a change request. The previous commits from version control repositories of these entities are then mined for expert developers. The role of the IR method in selectively reducing the mining space is different from previous approaches that textually index past change requests and/or commits. The approach is evaluated on change requests from three open‐source systems: ArgoUML, Eclipse, and KOffice, across a range of accuracy criteria. The results show that the overall accuracies of the correctly recommended developers are between 47 and 96% for bug reports, and between 43 and 60% for feature requests. Moreover, comparison results with two other recommendation alternatives show that the presented approach outperforms them with a substantial margin. Project leads or developers can use this approach in maintenance tasks immediately after the receipt of a change request in a free‐form text. Copyright