16,754 research outputs found

    Automatic Recall of Software Lessons Learned for Software Project Managers

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    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

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    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

    Searching for Relevant Lessons Learned Using Hybrid Information Retrieval Classifiers: A Case Study in Software Engineering

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    The lessons learned (LL) repository is one of the most valuable sources of knowledge for a software organization. It can provide distinctive guidance regarding previous working solutions for historical software management problems, or former success stories to be followed. However, the unstructured format of the LL repository makes it difficult to search using general queries, which are manually inputted by project managers (PMs). For this reason, this repository may often be overlooked despite the valuable information it provides. Since the LL repository targets PMs, the search method should be domain specific rather than generic as in the case of general web searching. In previous work, we provided an automatic information retrieval based LL classifier solution. In our solution, we relied on existing project management artifacts in constructing the search query on-the-fly. In this paper, we extend our previous work by examining the impact of the hybridization of multiple LL classifiers, from our previous study, on performance. We employ two of the hybridization techniques from the literature to construct the hybrid classifiers. An industrial dataset of 212 LL records is used for validation. The results show the superiority of the hybrid classifier over the top achieving individual classifier, which reached 25%

    The computerization of programming: Ada (R) lessons learned

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    One of the largest systems yet written in Ada has been constructed. This system is the Intermetrics Ada compiler. Many lessons have been learned during the implementation of this Ada compiler. Some of these lessons, concentrating on those lessons relevant to large system implementations are described. The characteristics of the Ada compiler implementation project at Intermetrics are also described. Some specific experiences during the implementation are pointed out

    The Contribution of a Model to Estimate Activities in Software Projects Based on Lessons Learned

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    Purpose – The main objective of this article is to propose the use of a model developed by Matturo and Silva (2010) to capture knowledge in software projects based on the lessons learned.Design/methodology/approach – We carried out a qualitative research from a descriptive perspective through a single case study applied to an Enterprise Information Technology company. The company is a leader in market solutions to support customer experience management. For the data collection process, we used systematic literature review, document analysis and semi-structured interviews.Findings – The results supported project managers to better understand the storage and use of information from lessons learned in dimensioning the use of human resources and to support the estimation of new project activities. In addition, the results showed the organization's disregard for not giving due importance to the information and knowledge generated during the life cycle of a project.Research, Practical & Social implications – The model allows companies to obtain new knowledge or consult existing knowledge throughout the life cycle of projects and to support project managers in the process of estimating activities and preparing budgets with greater precision, using the information from lessons learned as a support. acquired in the completed projects.Originality/value – The lack of information in the initial scope of the project and in the definition of activities in the human resource allocation process hinder the duration of the project's development activities, directly resulting in inaccurate estimates. As a result, this scenario contributes to the increased risk of deviations in terms and / or costs of software projects.

    FEATURE-BASED SENTIMENT ANALYSIS OF CODIFIED

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    Most project-based organizations possess extensive collections of diverse project documents. Exploring the knowledge codified in such project documents is specifically recommended by the common project management guidelines. In practice, however, project managers are faced with the problem of information overload when trying to analyze the extensive document collections. This paper addresses this problem by combining two approaches already established in other disciplines. The first involves the development of a Project Knowledge Dictionary (PKD) for the automated analysis of knowledge contents codified in project documents. The second involves the integration of a sentiment analysis where concrete opinion expressions (positive/negative) are identified in connection with the codified project knowledge. Building on this, three mutually complementary analyses are demonstrated, which provide the following contributions: (1) determining the volume and distribution of five project knowledge types in project documents; (2) determining the general sentiment (positive/negative) in conjunction with the textual description of the project knowledge; (3) classifying project documents by their sentiment. By this means, the proposed solution provides valuable insight into the emotional situation in projects and contributes to the emerging research issue of project sentiment analysis. Furthermore, the solution makes a contribution to overcoming the information overload by assessing and organizing the knowledge content of large document collections

    Maps of Lessons Learnt in Requirements Engineering

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    Both researchers and practitioners have emphasized the importance of learning from past experiences and its consequential impact on project time, cost, and quality. However, from the survey we conducted of requirements engineering (RE) practitioners, over 70\% of the respondents stated that they seldom use RE lessons in the RE process, though 85\% of these would use such lessons if readily available. Our observation, however, is that RE lessons are scattered, mainly implicitly, in the literature and practice, which obviously, does not help the situation. We, therefore, present ``maps” of RE lessons which would highlight weak (dark) and strong (bright) areas of RE (and hence RE theories). Such maps would thus be: (a) a driver for research to ``light up” the darker areas of RE and (b) a guide for practice to benefit from the brighter areas. To achieve this goal, we populated the maps with over 200 RE lessons elicited from literature and practice using a systematic literature review and survey. The results show that approximately 80\% of the elicited lessons are implicit and that approximately 70\% of the lessons deal with the elicitation, analysis, and specification RE phases only. The RE Lesson Maps, elicited lessons, and the results from populating the maps provide novel scientific groundings for lessons learnt in RE as this topic has not yet been systematically studied in the field

    Spacecraft software training needs assessment research, appendices

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    The appendices to the previously reported study are presented: statistical data from task rating worksheets; SSD references; survey forms; fourth generation language, a powerful, long-term solution to maintenance cost; task list; methodology; SwRI's instructional systems development model; relevant research; and references
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