The Experts below are selected from a list of 1914 Experts worldwide ranked by ideXlab platform

Mohamed, Tamer Mohamed Abdellatif - One of the best experts on this subject based on the ideXlab platform.

  • Automatic RecaLl of Software Lessons Learned for Software Project Managers
    Scholarship@Western, 2019
    Co-Authors: Mohamed, Tamer Mohamed Abdellatif, Capretz, Luiz Fernando, Ho Danny
    Abstract:

    Context: Lessons learned (Ll) records constitute the software organization memory of successes and failures. Ll are recorded within the organization repository for future reference to optimize planning, gain experience, and elevate market competitiveness. However, manuaLly searching this repository is a daunting task, so it is often disregarded. This can lead to the repetition of previous mistakes or even missing potential opportunities. This, in turn, can negatively affect the organization’s profitability and competitiveness. Objective: We aim to present a novel solution that provides an automatic process to recaLl relevant Ll and to push those Ll to project managers. This wiLl dramaticaLly save the time and effort of manuaLly searching the unstructured Ll repositories and thus encourage the Ll exploitation. Method: We exploit existing project artifacts to build the Ll search queries on-the-fly in order to bypass the tedious manual searching. An empirical case study is conducted to build the automatic Ll recaLl solution and evaluate its effectiveness. The study employs three of the most popular information retrieval models to construct the solution. Furthermore, a real-world dataset of 212 Ll records from 30 different software projects is used for validation. Top-k and MAP weLl-known accuracy metrics are used as weLl. Results: Our case study results confirm the effectiveness of the automatic Ll recaLl solution. Also, the results prove the success of using existing project artifacts to dynamicaLly build the search query string. This is supported by a discerning accuracy of about 70% achieved in the case of top-k. Conclusion: The automatic Ll recaLl solution is valid with high accuracy. It wiLl eliminate the effort needed to manuaLly search the Ll repository. Therefore, this wiLl positively encourage project managers to reuse the available Ll knowledge – which wiLl avoid old pitfaLls and unleash hidden business opportunities

  • Automatic RecaLl of Lessons Learned for Software Project Managers
    Scholarship@Western, 2018
    Co-Authors: Mohamed, Tamer Mohamed Abdellatif
    Abstract:

    Lessons learned (Ll) records constitute a software organization’s memory of successes and failures. Ll are recorded within the organization repository for future reference to optimize planning, gain experience, and elevate market competitiveness. However, manuaLly searching this repository is a daunting task, so it is often overlooked. This can lead to the repetition of previous mistakes and missing potential opportunities, which, in turn, can negatively affect the organization’s profitability and competitiveness. In this thesis, we present a novel solution that provides an automatic process to recaLl relevant Ll and to push them to project managers. This substantiaLly reduces the amount of time and effort required to manuaLly search the unstructured Ll repositories, and therefore, it encourages the utilization of Ll. In this study, we exploit existing project artifacts to build the Ll search queries on-the-fly, in order to bypass the tedious manual search process. While most of the current Ll recaLl studies rely on case-based reasoning, they have some limitations including the need to reformat the Ll repository, which is impractical, and the need for tight user involvement. This makes us the first to employ information retrieval (IR) to address the Ll recaLl. An empirical study has been conducted to build the automatic Ll recaLl solution and evaluate its effectiveness. In our study, we employ three of the most popular IR models to construct a solution that considers multiple classifier configurations. In addition, we have extended this study by examining the impact of the hybridization of Ll classifiers on the classifiers’ performance. Furthermore, a real-world dataset of 212 Ll records from 30 different software projects has been used for validation. Top-k and MAP, weLl-known accuracy metrics, have been used as weLl. The study results confirm the effectiveness of the automatic Ll recaLl solution by a discerning accuracy of about 70%, which was increased to 74% in the case of hybridization. This eliminates the effort needed to manuaLly search the Ll repository, which positively encourages project managers to reuse the available Ll knowledge – which in turn avoids old pitfaLls and unleash hidden business opportunities

Ho Danny - One of the best experts on this subject based on the ideXlab platform.

  • Automatic RecaLl of Software Lessons Learned for Software Project Managers
    Scholarship@Western, 2019
    Co-Authors: Mohamed, Tamer Mohamed Abdellatif, Capretz, Luiz Fernando, Ho Danny
    Abstract:

    Context: Lessons learned (Ll) records constitute the software organization memory of successes and failures. Ll are recorded within the organization repository for future reference to optimize planning, gain experience, and elevate market competitiveness. However, manuaLly searching this repository is a daunting task, so it is often disregarded. This can lead to the repetition of previous mistakes or even missing potential opportunities. This, in turn, can negatively affect the organization’s profitability and competitiveness. Objective: We aim to present a novel solution that provides an automatic process to recaLl relevant Ll and to push those Ll to project managers. This wiLl dramaticaLly save the time and effort of manuaLly searching the unstructured Ll repositories and thus encourage the Ll exploitation. Method: We exploit existing project artifacts to build the Ll search queries on-the-fly in order to bypass the tedious manual searching. An empirical case study is conducted to build the automatic Ll recaLl solution and evaluate its effectiveness. The study employs three of the most popular information retrieval models to construct the solution. Furthermore, a real-world dataset of 212 Ll records from 30 different software projects is used for validation. Top-k and MAP weLl-known accuracy metrics are used as weLl. Results: Our case study results confirm the effectiveness of the automatic Ll recaLl solution. Also, the results prove the success of using existing project artifacts to dynamicaLly build the search query string. This is supported by a discerning accuracy of about 70% achieved in the case of top-k. Conclusion: The automatic Ll recaLl solution is valid with high accuracy. It wiLl eliminate the effort needed to manuaLly search the Ll repository. Therefore, this wiLl positively encourage project managers to reuse the available Ll knowledge – which wiLl avoid old pitfaLls and unleash hidden business opportunities

Capretz, Luiz Fernando - One of the best experts on this subject based on the ideXlab platform.

  • Automatic RecaLl of Software Lessons Learned for Software Project Managers
    Scholarship@Western, 2019
    Co-Authors: Mohamed, Tamer Mohamed Abdellatif, Capretz, Luiz Fernando, Ho Danny
    Abstract:

    Context: Lessons learned (Ll) records constitute the software organization memory of successes and failures. Ll are recorded within the organization repository for future reference to optimize planning, gain experience, and elevate market competitiveness. However, manuaLly searching this repository is a daunting task, so it is often disregarded. This can lead to the repetition of previous mistakes or even missing potential opportunities. This, in turn, can negatively affect the organization’s profitability and competitiveness. Objective: We aim to present a novel solution that provides an automatic process to recaLl relevant Ll and to push those Ll to project managers. This wiLl dramaticaLly save the time and effort of manuaLly searching the unstructured Ll repositories and thus encourage the Ll exploitation. Method: We exploit existing project artifacts to build the Ll search queries on-the-fly in order to bypass the tedious manual searching. An empirical case study is conducted to build the automatic Ll recaLl solution and evaluate its effectiveness. The study employs three of the most popular information retrieval models to construct the solution. Furthermore, a real-world dataset of 212 Ll records from 30 different software projects is used for validation. Top-k and MAP weLl-known accuracy metrics are used as weLl. Results: Our case study results confirm the effectiveness of the automatic Ll recaLl solution. Also, the results prove the success of using existing project artifacts to dynamicaLly build the search query string. This is supported by a discerning accuracy of about 70% achieved in the case of top-k. Conclusion: The automatic Ll recaLl solution is valid with high accuracy. It wiLl eliminate the effort needed to manuaLly search the Ll repository. Therefore, this wiLl positively encourage project managers to reuse the available Ll knowledge – which wiLl avoid old pitfaLls and unleash hidden business opportunities