The Experts below are selected from a list of 858 Experts worldwide ranked by ideXlab platform

Rocco Oliveto - One of the best experts on this subject based on the ideXlab platform.

  • Predicting Query Quality for Applications of Text Retrieval to Software Engineering Tasks
    ACM Transactions on Software Engineering and Methodology, 2017
    Co-Authors: Chris Mills, Sonia Haiduc, Andrian Marcus, Gabriele Bavota, Rocco Oliveto, Andrea De Lucia
    Abstract:

    Context: Since the mid-2000s, numerous recommendation systems based on text retrieval (TR) have been proposed to support software engineering (SE) tasks such as concept location, Traceability Link recovery, code reuse, impact analysis, and so on. The success of TR-based solutions highly depends on the query submitted, which is either formulated by the developer or automatically extracted from software artifacts. Aim: We aim at predicting the quality of queries submitted to TR-based approaches in SE. This can lead to benefits for developers and for the quality of software systems alike. For example, knowing when a query is poorly formulated can save developers the time and frustration of analyzing irrelevant search results. Instead, they could focus on reformulating the query. Also, knowing if an artifact used as a query leads to irrelevant search results may uncover underlying problems in the query artifact itself. Method: We introduce an automatic query quality prediction approach for software artifact retrieval by adapting NL-inspired solutions to their use on software data. We present two applications and evaluations of the approach in the context of concept location and Traceability Link recovery, where TR has been applied most often in SE. For concept location, we use the approach to determine if the list of retrieved code elements is likely to contain code relevant to a particular change request or not, in which case, the queries are good candidates for reformulation. For Traceability Link recovery, the queries represent software artifacts. In this case, we use the query quality prediction approach to identify artifacts that are hard to trace to other artifacts and may therefore have a low intrinsic quality for TR-based Traceability Link recovery. Results: For concept location, the evaluation shows that our approach is able to correctly predict the quality of queries in 82% of the cases, on average, using very little training data. In the case of Traceability recovery, the proposed approach is able to detect hard to trace artifacts in 74% of the cases, on average. Conclusions: The results of our evaluation on applications for concept location and Traceability Link recovery indicate that our approach can be used to predict the results of a TR-based approach by assessing the quality of the text query. This can lead to saved effort and time, as well as the identification of software artifacts that may be difficult to trace using TR.

  • Parameterizing and Assembling IR-Based Solutions for SE Tasks Using Genetic Algorithms
    2016 IEEE 23rd International Conference on Software Analysis Evolution and Reengineering (SANER), 2016
    Co-Authors: Annibale Panichella, Denys Poshyvanyk, Rocco Oliveto, Massimiliano Di Penta, Andrea De Lucia
    Abstract:

    Information Retrieval (IR) approaches are nowadays used to support various software engineering tasks, such as feature location, Traceability Link recovery, clone detection, or refactoring. However, previous studies showed that inadequate instantiation of an IR technique and underlying process could significantly affect the performance of such approaches in terms of precision and recall. This paper proposes the use of Genetic Algorithms (GAs) to automatically configure and assemble an IR process for software engineering tasks. The approach (named GA-IR) determines the (near) optimal solution to be used for each stage of the IR process, i.e., term extraction, stop word removal, stemming, indexing and an IR algebraic method calibration. We applied GA-IR on two different software engineering tasks, namely Traceability Link recovery and identification of duplicate bug reports. The results of the study indicate that GA-IR outperforms approaches previously published in the literature, and that it does not significantly differ from an ideal upper bound that could be achieved by a supervised and combinatorial approach.

  • SANER - Parameterizing and Assembling IR-Based Solutions for SE Tasks Using Genetic Algorithms
    2016 IEEE 23rd International Conference on Software Analysis Evolution and Reengineering (SANER), 2016
    Co-Authors: Annibale Panichella, Denys Poshyvanyk, Rocco Oliveto, Massimiliano Di Penta, Andrea De Lucia
    Abstract:

    Information Retrieval (IR) approaches are nowadays used to support various software engineering tasks, such as feature location, Traceability Link recovery, clone detection, or refactoring. However, previous studies showed that inadequate instantiation of an IR technique and underlying process could significantly affect the performance of such approaches in terms of precision and recall. This paper proposes the use of Genetic Algorithms (GAs) to automatically configure and assemble an IR process for software engineering tasks. The approach (named GA-IR) determines the (near) optimal solution to be used for each stage of the IR process, i.e., term extraction, stop word removal, stemming, indexing and an IR algebraic method calibration. We applied GA-IR on two different software engineering tasks, namely Traceability Link recovery and identification of duplicate bug reports. The results of the study indicate that GA-IR outperforms approaches previously published in the literature, and that it does not significantly differ from an ideal upper bound that could be achieved by a supervised and combinatorial approach.

  • Using code ownership to improve IR-based Traceability Link Recovery
    2013 21st International Conference on Program Comprehension (ICPC), 2013
    Co-Authors: Diana Diaz, Andrian Marcus, Gabriele Bavota, Rocco Oliveto, Silvia Takahashi, Andrea De Lucia
    Abstract:

    Information Retrieval (IR) techniques have gained wide-spread acceptance as a method for automating Traceability recovery. These techniques recover Links between software artifacts based on their textual similarity, i.e., the higher the similarity, the higher the likelihood that there is a Link between the two artifacts. A common problem with all IR-based techniques is filtering out noise from the list of candidate Links, in order to improve the recovery accuracy. Indeed, software artifacts may be related in many ways and the textual information captures only one aspect of their relationships. In this paper we propose to leverage code ownership information to capture relationships between source code artifacts for improving the recovery of Traceability Links between documentation and source code. Specifically, we extract the author of each source code component and for each author we identify the “context” she worked on. Thus, for a given query from the external documentation we compute the similarity between it and the context of the authors. When retrieving classes that relate to a specific query using a standard IR-based approach we reward all the classes developed by the authors having their context most similar to the query, by boosting their similarity to the query. The proposed approach, named TYRION (Traceability Link Recovery using Information retrieval and code OwNership), has been instantiated for the recovery of Traceability Links between use cases and Java classes of two software systems. The results indicate that code ownership information can be used to improve the accuracy of an IR-based Traceability Link recovery technique.

  • ICPC - Using code ownership to improve IR-based Traceability Link Recovery
    2013 21st International Conference on Program Comprehension (ICPC), 2013
    Co-Authors: Diana Diaz, Andrian Marcus, Gabriele Bavota, Rocco Oliveto, Silvia Takahashi, Andrea De Lucia
    Abstract:

    Information Retrieval (IR) techniques have gained wide-spread acceptance as a method for automating Traceability recovery. These techniques recover Links between software artifacts based on their textual similarity, i.e., the higher the similarity, the higher the likelihood that there is a Link between the two artifacts. A common problem with all IR-based techniques is filtering out noise from the list of candidate Links, in order to improve the recovery accuracy. Indeed, software artifacts may be related in many ways and the textual information captures only one aspect of their relationships. In this paper we propose to leverage code ownership information to capture relationships between source code artifacts for improving the recovery of Traceability Links between documentation and source code. Specifically, we extract the author of each source code component and for each author we identify the “context” she worked on. Thus, for a given query from the external documentation we compute the similarity between it and the context of the authors. When retrieving classes that relate to a specific query using a standard IR-based approach we reward all the classes developed by the authors having their context most similar to the query, by boosting their similarity to the query. The proposed approach, named TYRION (Traceability Link Recovery using Information retrieval and code OwNership), has been instantiated for the recovery of Traceability Links between use cases and Java classes of two software systems. The results indicate that code ownership information can be used to improve the accuracy of an IR-based Traceability Link recovery technique.

Stephen W Thomas - One of the best experts on this subject based on the ideXlab platform.

  • Mining unstructured software repositories
    Evolving Software Systems, 2014
    Co-Authors: Stephen W Thomas, Ahmed E Hassan, Dorothea Blostein
    Abstract:

    Mining software repositories, which is the process of analyzing the data related to software development practices, is an emerging field of research which aims to improve software evolutionary tasks. The data in many software repositories is unstructured (for example, the natural language text in bug reports), making it particularly difficult to mine and analyze. In this chapter, we survey tools and techniques for mining unstructured software repositories, with a focus on information retrieval models. In addition, we discuss several software engineering tasks that can be enhanced by leveraging unstructured data, including bug prediction, clone detection, bug triage, feature location, code search engines, Traceability Link recovery, evolution and trend analysis, bug localization, and more. Finally, we provide a hands-on tutorial for using an IR model on an unstructured repository to perform a software engineering task.

  • Evolving Software Systems - Mining Unstructured Software Repositories
    Evolving Software Systems, 2013
    Co-Authors: Stephen W Thomas, Ahmed E Hassan, Dorothea Blostein
    Abstract:

    Mining software repositories, which is the process of analyzing the data related to software development practices, is an emerging field of research which aims to improve software evolutionary tasks. The data in many software repositories is unstructured (for example, the natural language text in bug reports), making it particularly difficult to mine and analyze. In this chapter, we survey tools and techniques for mining unstructured software repositories, with a focus on information retrieval models. In addition, we discuss several software engineering tasks that can be enhanced by leveraging unstructured data, including bug prediction, clone detection, bug triage, feature location, code search engines, Traceability Link recovery, evolution and trend analysis, bug localization, and more. Finally, we provide a hands-on tutorial for using an IR model on an unstructured repository to perform a software engineering task.

  • Mining software repositories using topic models
    2011 33rd International Conference on Software Engineering (ICSE), 2011
    Co-Authors: Stephen W Thomas
    Abstract:

    Software repositories, such as source code, email archives, and bug databases, contain unstructured and unlabeled text that is difficult to analyze with traditional techniques. We propose the use of statistical topic models to automatically discover structure in these textual repositories. This discovered structure has the potential to be used in software engineering tasks, such as bug prediction and Traceability Link recovery. Our research goal is to address the challenges of applying topic models to software repositories.

  • ICSE - Mining software repositories using topic models
    Proceeding of the 33rd international conference on Software engineering - ICSE '11, 2011
    Co-Authors: Stephen W Thomas
    Abstract:

    Software repositories, such as source code, email archives, and bug databases, contain unstructured and unlabeled text that is difficult to analyze with traditional techniques. We propose the use of statistical topic models to automatically discover structure in these textual repositories. This discovered structure has the potential to be used in software engineering tasks, such as bug prediction and Traceability Link recovery. Our research goal is to address the challenges of applying topic models to software repositories.

Andrea De Lucia - One of the best experts on this subject based on the ideXlab platform.

  • Predicting Query Quality for Applications of Text Retrieval to Software Engineering Tasks
    ACM Transactions on Software Engineering and Methodology, 2017
    Co-Authors: Chris Mills, Sonia Haiduc, Andrian Marcus, Gabriele Bavota, Rocco Oliveto, Andrea De Lucia
    Abstract:

    Context: Since the mid-2000s, numerous recommendation systems based on text retrieval (TR) have been proposed to support software engineering (SE) tasks such as concept location, Traceability Link recovery, code reuse, impact analysis, and so on. The success of TR-based solutions highly depends on the query submitted, which is either formulated by the developer or automatically extracted from software artifacts. Aim: We aim at predicting the quality of queries submitted to TR-based approaches in SE. This can lead to benefits for developers and for the quality of software systems alike. For example, knowing when a query is poorly formulated can save developers the time and frustration of analyzing irrelevant search results. Instead, they could focus on reformulating the query. Also, knowing if an artifact used as a query leads to irrelevant search results may uncover underlying problems in the query artifact itself. Method: We introduce an automatic query quality prediction approach for software artifact retrieval by adapting NL-inspired solutions to their use on software data. We present two applications and evaluations of the approach in the context of concept location and Traceability Link recovery, where TR has been applied most often in SE. For concept location, we use the approach to determine if the list of retrieved code elements is likely to contain code relevant to a particular change request or not, in which case, the queries are good candidates for reformulation. For Traceability Link recovery, the queries represent software artifacts. In this case, we use the query quality prediction approach to identify artifacts that are hard to trace to other artifacts and may therefore have a low intrinsic quality for TR-based Traceability Link recovery. Results: For concept location, the evaluation shows that our approach is able to correctly predict the quality of queries in 82% of the cases, on average, using very little training data. In the case of Traceability recovery, the proposed approach is able to detect hard to trace artifacts in 74% of the cases, on average. Conclusions: The results of our evaluation on applications for concept location and Traceability Link recovery indicate that our approach can be used to predict the results of a TR-based approach by assessing the quality of the text query. This can lead to saved effort and time, as well as the identification of software artifacts that may be difficult to trace using TR.

  • Parameterizing and Assembling IR-Based Solutions for SE Tasks Using Genetic Algorithms
    2016 IEEE 23rd International Conference on Software Analysis Evolution and Reengineering (SANER), 2016
    Co-Authors: Annibale Panichella, Denys Poshyvanyk, Rocco Oliveto, Massimiliano Di Penta, Andrea De Lucia
    Abstract:

    Information Retrieval (IR) approaches are nowadays used to support various software engineering tasks, such as feature location, Traceability Link recovery, clone detection, or refactoring. However, previous studies showed that inadequate instantiation of an IR technique and underlying process could significantly affect the performance of such approaches in terms of precision and recall. This paper proposes the use of Genetic Algorithms (GAs) to automatically configure and assemble an IR process for software engineering tasks. The approach (named GA-IR) determines the (near) optimal solution to be used for each stage of the IR process, i.e., term extraction, stop word removal, stemming, indexing and an IR algebraic method calibration. We applied GA-IR on two different software engineering tasks, namely Traceability Link recovery and identification of duplicate bug reports. The results of the study indicate that GA-IR outperforms approaches previously published in the literature, and that it does not significantly differ from an ideal upper bound that could be achieved by a supervised and combinatorial approach.

  • SANER - Parameterizing and Assembling IR-Based Solutions for SE Tasks Using Genetic Algorithms
    2016 IEEE 23rd International Conference on Software Analysis Evolution and Reengineering (SANER), 2016
    Co-Authors: Annibale Panichella, Denys Poshyvanyk, Rocco Oliveto, Massimiliano Di Penta, Andrea De Lucia
    Abstract:

    Information Retrieval (IR) approaches are nowadays used to support various software engineering tasks, such as feature location, Traceability Link recovery, clone detection, or refactoring. However, previous studies showed that inadequate instantiation of an IR technique and underlying process could significantly affect the performance of such approaches in terms of precision and recall. This paper proposes the use of Genetic Algorithms (GAs) to automatically configure and assemble an IR process for software engineering tasks. The approach (named GA-IR) determines the (near) optimal solution to be used for each stage of the IR process, i.e., term extraction, stop word removal, stemming, indexing and an IR algebraic method calibration. We applied GA-IR on two different software engineering tasks, namely Traceability Link recovery and identification of duplicate bug reports. The results of the study indicate that GA-IR outperforms approaches previously published in the literature, and that it does not significantly differ from an ideal upper bound that could be achieved by a supervised and combinatorial approach.

  • Using code ownership to improve IR-based Traceability Link Recovery
    2013 21st International Conference on Program Comprehension (ICPC), 2013
    Co-Authors: Diana Diaz, Andrian Marcus, Gabriele Bavota, Rocco Oliveto, Silvia Takahashi, Andrea De Lucia
    Abstract:

    Information Retrieval (IR) techniques have gained wide-spread acceptance as a method for automating Traceability recovery. These techniques recover Links between software artifacts based on their textual similarity, i.e., the higher the similarity, the higher the likelihood that there is a Link between the two artifacts. A common problem with all IR-based techniques is filtering out noise from the list of candidate Links, in order to improve the recovery accuracy. Indeed, software artifacts may be related in many ways and the textual information captures only one aspect of their relationships. In this paper we propose to leverage code ownership information to capture relationships between source code artifacts for improving the recovery of Traceability Links between documentation and source code. Specifically, we extract the author of each source code component and for each author we identify the “context” she worked on. Thus, for a given query from the external documentation we compute the similarity between it and the context of the authors. When retrieving classes that relate to a specific query using a standard IR-based approach we reward all the classes developed by the authors having their context most similar to the query, by boosting their similarity to the query. The proposed approach, named TYRION (Traceability Link Recovery using Information retrieval and code OwNership), has been instantiated for the recovery of Traceability Links between use cases and Java classes of two software systems. The results indicate that code ownership information can be used to improve the accuracy of an IR-based Traceability Link recovery technique.

  • ICPC - Using code ownership to improve IR-based Traceability Link Recovery
    2013 21st International Conference on Program Comprehension (ICPC), 2013
    Co-Authors: Diana Diaz, Andrian Marcus, Gabriele Bavota, Rocco Oliveto, Silvia Takahashi, Andrea De Lucia
    Abstract:

    Information Retrieval (IR) techniques have gained wide-spread acceptance as a method for automating Traceability recovery. These techniques recover Links between software artifacts based on their textual similarity, i.e., the higher the similarity, the higher the likelihood that there is a Link between the two artifacts. A common problem with all IR-based techniques is filtering out noise from the list of candidate Links, in order to improve the recovery accuracy. Indeed, software artifacts may be related in many ways and the textual information captures only one aspect of their relationships. In this paper we propose to leverage code ownership information to capture relationships between source code artifacts for improving the recovery of Traceability Links between documentation and source code. Specifically, we extract the author of each source code component and for each author we identify the “context” she worked on. Thus, for a given query from the external documentation we compute the similarity between it and the context of the authors. When retrieving classes that relate to a specific query using a standard IR-based approach we reward all the classes developed by the authors having their context most similar to the query, by boosting their similarity to the query. The proposed approach, named TYRION (Traceability Link Recovery using Information retrieval and code OwNership), has been instantiated for the recovery of Traceability Links between use cases and Java classes of two software systems. The results indicate that code ownership information can be used to improve the accuracy of an IR-based Traceability Link recovery technique.

Huzefa Kagdi - One of the best experts on this subject based on the ideXlab platform.

  • Software Repositories: A Source for Traceability Links
    2020
    Co-Authors: Huzefa Kagdi, Jonathan I Maletic
    Abstract:

    paper analyzes six open source projects in order to assess software repositories, such as those managed by Subversion, as a source for uncovering/discovering Traceability Links between different types of software artifacts. Our finding suggests that software repositories store a variety of artifacts that are central to open source development and use. Furthermore, a heuristic-based approach that uses sequential-pattern mining is presented. This approach analyzes commits in a version history to mine for highly frequent co-occurring changes to different artifacts (e.g., source code and documentation). The hypothesis is if different types of artifacts are committed together frequently then there is a high probability that they have a Traceability Link between them. Examples of mined Traceability Links from our preliminary experimentation on mining KDE (K Desktop Environment) repositories are presented.

  • Eye movements in software Traceability Link recovery
    Empirical Software Engineering, 2017
    Co-Authors: Bonita Sharif, John Meinken, Timothy Shaffer, Huzefa Kagdi
    Abstract:

    Information Retrieval (IR) approaches, such as Latent Semantic Indexing (LSI) and Vector Space Model (VSM), are commonly applied to recover software Traceability Links. Recently, an approach based on developers’ eye gazes was proposed to retrieve Traceability Links. This paper presents a comparative study on IR and eye-gaze based approaches. In addition, it reports on the possibility of using eye gaze Links as an alternative benchmark in comparison to commits. The study conducted asked developers to perform bug-localization tasks on the open source subject system JabRef. The iTrace environment, which is an eye tracking enabled Eclipse plugin, was used to collect eye gaze data. During the data collection phase, an eye tracker was used to gather the source code entities (SCE’s), developers looked at while solving these tasks. We present an algorithm that uses the collected gaze dataset to produce candidate Traceability Links related to the tasks. In the evaluation phase, we compared the results of our algorithm with the results of an IR technique, in two different contexts. In the first context, precision and recall metric values are reported for both IR and eye gaze approaches based on commits. In the second context, another set of developers were asked to rate the candidate Links from each of the two techniques in terms of how useful they were in fixing the bugs. The eye gaze approach outperforms standard LSI and VSM approaches and reports a 55 % precision and 67 % recall on average for all tasks when compared to how the developers actually fixed the bug. In the second context, the usefulness results show that Links generated by our algorithm were considered to be significantly more useful (to fix the bug) than those of the IR technique in a majority of tasks. We discuss the implications of this radically different method of deriving Traceability Links. Techniques for feature location/bug localization are commonly evaluated on benchmarks formed from commits as is done in the evaluation phase of this study. Although, commits are a reasonable source, they only capture entities that were eventually changed to fix a bug or resolve a feature. We investigate another type of benchmark based on eye tracking data, namely Links generated from the bug-localization tasks given to the developers in the data collection phase. The source code entities relevant to subjected bugs recommended from IR methods are evaluated on both commits and Links generated from eye gaze. The results of the benchmarking phase show that the use of eye tracking could form an effective (complementary) benchmark and add another interesting perspective in the evaluation of bug-localization techniques.

  • EFSE@ICSE - On the use of eye tracking in software Traceability
    Proceeding of the 6th international workshop on Traceability in emerging forms of software engineering - TEFSE '11, 2011
    Co-Authors: Bonita Sharif, Huzefa Kagdi
    Abstract:

    The paper advocates for the induction of eye tracking technology in software Traceability and takes a position that the use of eye tracking metrics can contribute to several software Traceability tasks. The authors posit that the role of eye tracking is not simply restricted to an instrument for empirical studies, but also could extend to providing a foundation of a new software Traceability methodology. Several scenarios where eye-tracking metrics could be meaningful are presented. The specific research directions include conducting empirical studies with eye-tracking metrics and replicating previously reported empirical studies, eye-tracking enabled Traceability Link recovery and management methodology, and visualization support.

  • Mining software repositories for Traceability Links
    15th IEEE International Conference on Program Comprehension (ICPC '07), 2007
    Co-Authors: Huzefa Kagdi, Jonathan I Maletic, Bonita Sharif
    Abstract:

    An approach to recover/discover Traceability Links between software artifacts via the examination of a software system's version history is presented. A heuristic-based approach that uses sequential-pattern mining is applied to the commits in software repositories for uncovering highly frequent co-changing sets of artifacts (e.g., source code and documentation). If different types of files are committed together with high frequency then there is a high probability that they have a Traceability Link between them. The approach is evaluated on a number of versions of the open source system KDE. As a validation step, the discovered Links are used to predict similar changes in the newer versions of the same system. The results show highly precision predictions of certain types of Traceability Links.

  • ICPC - Mining software repositories for Traceability Links
    15th IEEE International Conference on Program Comprehension (ICPC '07), 2007
    Co-Authors: Huzefa Kagdi, Jonathan I Maletic, Bonita Sharif
    Abstract:

    An approach to recover/discover Traceability Links between software artifacts via the examination of a software system's version history is presented. A heuristic-based approach that uses sequential-pattern mining is applied to the commits in software repositories for uncovering highly frequent co-changing sets of artifacts (e.g., source code and documentation). If different types of files are committed together with high frequency then there is a high probability that they have a Traceability Link between them. The approach is evaluated on a number of versions of the open source system KDE. As a validation step, the discovered Links are used to predict similar changes in the newer versions of the same system. The results show highly precision predictions of certain types of Traceability Links.

Hironori Washizaki - One of the best experts on this subject based on the ideXlab platform.

  • COMPSAC (2) - Traceability Link Mining — Focusing on Usability
    2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), 2017
    Co-Authors: Yukiya Yazawa, Shinpei Ogata, Haruhiko Kaiya, Kozo Okano, Hironori Washizaki
    Abstract:

    The recovery of Traceability Links to requirements from a functional model created through analysis/design is crucial to understand existing systems for reuse, improvement or maintenance. Functional and non-functional requirements generally are implicitly interpreted and non-systematically woven into a functional model by analysts/designers. Traditional Traceability Link recovery methods focusing on terminology or syntactic structure, however, have a recovery limit because such interpretation and weave are not paid enough attention. This paper presents a novel idea to decompose such a functional model into model components in order to accurately trace a components to requirements especially non-functional requirements. We call such the decomposition Traceability Link mining. A screen transition model is adopted as the functional model because of the focus on usability.

  • Traceability Link mining focusing on usability
    Computer Software and Applications Conference, 2017
    Co-Authors: Yukiya Yazawa, Shinpei Ogata, Haruhiko Kaiya, Kozo Okano, Hironori Washizaki
    Abstract:

    The recovery of Traceability Links to requirements from a functional model created through analysis/design is crucial to understand existing systems for reuse, improvement or maintenance. Functional and non-functional requirements generally are implicitly interpreted and non-systematically woven into a functional model by analysts/designers. Traditional Traceability Link recovery methods focusing on terminology or syntactic structure, however, have a recovery limit because such interpretation and weave are not paid enough attention. This paper presents a novel idea to decompose such a functional model into model components in order to accurately trace a components to requirements especially non-functional requirements. We call such the decomposition Traceability Link mining. A screen transition model is adopted as the functional model because of the focus on usability.

  • Traceability Link Mining — Focusing on Usability
    2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), 2017
    Co-Authors: Yukiya Yazawa, Shinpei Ogata, Haruhiko Kaiya, Kozo Okano, Hironori Washizaki
    Abstract:

    The recovery of Traceability Links to requirements from a functional model created through analysis/design is crucial to understand existing systems for reuse, improvement or maintenance. Functional and non-functional requirements generally are implicitly interpreted and non-systematically woven into a functional model by analysts/designers. Traditional Traceability Link recovery methods focusing on terminology or syntactic structure, however, have a recovery limit because such interpretation and weave are not paid enough attention. This paper presents a novel idea to decompose such a functional model into model components in order to accurately trace a components to requirements especially non-functional requirements. We call such the decomposition Traceability Link mining. A screen transition model is adopted as the functional model because of the focus on usability.