Andrian Marcus

Recovering documentation-to-source-code traceability links using latent semantic indexing

An information retrieval technique, latent semantic indexing, is used to automatically identify traceability links from system documentation to program source code. The results of two experiments to identify links in existing software systems (i.e., the LEDA library, and Albergate) are presented. These results are compared with other similar type experimental results of traceability link identification using different types of information retrieval techniques. The method presented proves to give good results by comparison and additionally it is a low cost, highly flexible method to apply with regards to preprocessing and/or parsing of the source code and documentation.

An information retrieval approach to concept location in source code

Concept location identifies parts of a software system that implement a specific concept that originates from the problem or the solution domain. Concept location is a very common software engineering activity that directly supports software maintenance and evolution tasks such as incremental change and reverse engineering. This work addresses the problem of concept location using an advanced information retrieval method, Latent Semantic Indexing (LSI). LSI is used to map concepts expressed in natural language by the programmer to the relevant parts of the source code. Results of a case study on NCSA Mosaic are presented and compared with previously published results of other static methods for concept location.

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

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

Using the Conceptual Cohesion of Classes for Fault Prediction in Object-Oriented Systems

High cohesion is a desirable property of software as it positively impacts understanding, reuse, and maintenance. Currently proposed measures for cohesion in Object-Oriented (OO) software reflect particular interpretations of cohesion and capture different aspects of it. Existing approaches are largely based on using the structural information from the source code, such as attribute references, in methods to measure cohesion. This paper proposes a new measure for the cohesion of classes in OO software systems based on the analysis of the unstructured information embedded in the source code, such as comments and identifiers. The measure, named the Conceptual Cohesion of Classes (C3), is inspired by the mechanisms used to measure textual coherence in cognitive psychology and computational linguistics. This paper presents the principles and the technology that stand behind the C3 measure. A large case study on three open source software systems is presented which compares the new measure with an extensive set of existing metrics and uses them to construct models that predict software faults. The case study shows that the novel measure captures different aspects of class cohesion compared to any of the existing cohesion measures. In addition, combining C3 with existing structural cohesion metrics proves to be a better predictor of faulty classes when compared to different combinations of structural cohesion metrics.

Automated severity assessment of software defect reports

In mission critical systems, such as those developed by NASA, it is very important that the test engineers properly recognize the severity of each issue they identify during testing. Proper severity assessment is essential for appropriate resource allocation and planning for fixing activities and additional testing. Severity assessment is strongly influenced by the experience of the test engineers and by the time they spend on each issue. The paper presents a new and automated method named SEVERIS (severity issue assessment), which assists the test engineer in assigning severity levels to defect reports. SEVERIS is based on standard text mining and machine learning techniques applied to existing sets of defect reports. A case study on using SEVERIS with data from NASApsilas Project and Issue Tracking System (PITS) is presented in the paper. The case study results indicate that SEVERIS is a good predictor for issue severity levels, while it is easy to use and efficient.

Feature location via information retrieval based filtering of a single scenario execution trace

The paper presents a semi-automated technique for feature location in source code. The technique is based on combining information from two different sources: an execution trace, on one hand and the comments and identifiers from the source code, on the other hand. Users execute a single partial scenario, which exercises the desired feature and all executed methods are identified based on the collected trace. The source code is indexed using Latent Semantic Indexing, an Information Retrieval method, which allows users to write queries relevant to the desired feature and rank all the executed methods based on their textual similarity to the query. Two case studies on open source software (JEdit and Eclipse) indicate that the new technique has high accuracy, comparable with previously published approaches and it is easy to use as it considerably simplifies the dynamic analysis.

Using information retrieval based coupling measures for impact analysis

Coupling is an important property of software systems, which directly impacts program comprehension. In addition, the strength of coupling measured between modules in software is often used as a predictor of external software quality attributes such as changeability, ripple effects of changes and fault-proneness. This paper presents a new set of coupling measures for Object-Oriented (OO) software systems measuring conceptual coupling of classes. Conceptual coupling is based on measuring the degree to which the identifiers and comments from different classes relate to each other. This type of relationship, called conceptual coupling, is measured through the use of Information Retrieval (IR) techniques. The proposed measures are different from existing coupling measures and they capture new dimensions of coupling, which are not captured by the existing coupling measures. The paper investigates the use of the conceptual coupling measures during change impact analysis. The paper reports the findings of a case study in the source code of the Mozilla web browser, where the conceptual coupling metrics were compared to nine existing structural coupling metrics and proved to be better predictors for classes impacted by changes.

3D representations for software visualization

The paper presents a new 3D representation for visualizing large software systems. The origins of this representation can be directly traced to the SeeSoft metaphor. This work extends these visualization mechanisms by utilizing the third dimension, texture, abstraction mechanism, and by supporting new manipulation techniques and user interfaces. By utilizing a 3D representation we can better represent higher dimensional data than previous 2D views. An overview of our prototype tool and its basic functionality is given. Applications of this method to particular software engineering tasks are also discussed.

RECOVERY OF TRACEABILITY LINKS BETWEEN SOFTWARE DOCUMENTATION AND SOURCE CODE

An approach for the semi-automated recovery of traceability links between software documentation and source code is presented. The methodology is based on the application of information retrieval techniques to extract and analyze the semantic information from the source code and associated documentation. A semi-automatic process is defined based on the proposed methodology. The paper advocates the use of latent semantic indexing (LSI) as the supporting information retrieval technique. Two case studies using existing software are presented comparing this approach with others. The case studies show positive results for the proposed approach, especially considering the flexibility of the methods used.

The conceptual cohesion of classes

While often defined in informal ways, software cohesion reflects important properties of modules in a software system. Cohesion measurement has been used for quality assessment, fault proneness prediction, software modularization, etc. Existing approaches to cohesion measurement in object-oriented software are largely based on the structural information of the source code, such as attribute references in methods. These measures reflect particular interpretations of cohesion and try to capture different aspects of cohesion and no single cohesion metric or suite is accepted as standard measurement for cohesion. The paper proposes a new set of measures for the cohesion of individual classes within an OO software system, based on the analysis of the semantic information embedded in the source code, such as comments and identifiers. A case study on open source software is presented, which compares the new measures with an extensive set of existing metrics. The differences and similarities among the approaches and results are discussed and analyzed.