Accepted Papers


  • Towards A Multi - Feature Enabled Approach For Optimized Expert Seeking
    Mariam Abdullah1, Hassan Noureddine1, Jawad Makki1, Hussein Charara1, Hussein Hazimeh2, Omar Abou Khaled2 and Elena Mugellini2, 1Lebanese University, Beirut, Lebanon, 2HES-SO/FR, Fribourg, Switzerland
    With the enormous growth of data, retrieving information from the Web became more desirable and even more challenging because of the Big Data issues (e.g. noise, corruption, bad quality...etc.). Expert seeking, defined as returning a ranked list of expert researchers given a topic, has been a real concern in the last 15 years. This kind of task comes in handy when building scientific committees, requiring to identify the scholars' experience to assign them the most suitable roles in addition to other factors as well. Due to the fact the Web is drowning with plenty of data, this opens up the opportunity to collect different kinds of expertise evidence. In this paper, we propose an expert seeking approach with specifying the most desirable features (i.e. criteria on which researcher's evaluation is done) along with their estimation techniques. We utilized some machine learning techniques in our system and we aim at verifying the effectiveness of incorporating influential features that go beyond publications.
  • Need for A Soft Dimension
    Pradeep Waychal1 and Luiz Fernando Capretz2, 1College of Engineering, Innovation Center, India and 2Western University, Canada
    It is impossible to separate the human factors from software engineering expertise during software development, because software is developed by people and for people. The intangible nature of software has made it a difficult product to successfully create, and an examination of the many reasons for major software system failures show that the reasons for failures eventually come down to human issues. Software developers, immersed as they are in the technological aspect of the product, can quickly learn lessons from technological failures and readily come up with solutions to avoid them in the future, yet they do not learn lessons from human aspects in software engineering. Dealing with human errors is much more difficult for developers and often this aspect is overlooked in the evaluation process as developers move on to issues that they are more comfortable solving. A major reason for this oversight is that software psychology (the softer side) has not developed as extensively
  • A Conceptual Framework For Agile Planning Practices For Software Vendor Environment
    Ramesh Lal and Parma Nand, Auckland University of Technology, New Zealand
    There is a misconception that agile development requires minimal planning effort. In reality, agile approach for the market-driven software development requires highly disciplined, reliable and accurate planning practices to swiftly plan and develop high-value innovations. This study investigated a highly successful international software vendor based in Melbourne, Australia to provide a case study research on agile planning practices. This investigation highlights a number of organizational structures, roles, and skills, which software vendors must adopt for successful agile planning practices in a software vendor environment. These changes are driven by agile philosophies such as adaptation, cross-functional collaboration and empowerment/delegation. We constructed a conceptual framework, the Conceptual Framework for Agile Planning Practices, illustrating the three levels of agile planning in a software vendor environment. It provided the basis for data collection and analysis- as a result of our findings, we enhanced the Conceptual Framework for Agile Planning Practices.
  • Estimating Handling Time of Software Defects
    George Kourz1, Shaul Strachan2 and Raz Regevx2, 1Hewlett Packard Labs, Israel, 2Hewlett Packard Enterprise, Israel
    The problem of accurately predicting handling time for software defects is of great practical importance. However, it is dicult to suggest a practical generic algorithm for such estimates, due in part to the limited information available when opening a defect and the lack of a uniform standard for defect structure. We suggest an algorithm to address these challenges that is implementable over di erent defect management tools. Our algorithm uses machine learning regression techniques to predict the handling time of defects based on past behaviour of similar defects. The algorithm relies only on a minimal set of assumptions about the structure of the input data. We show how an implementation of this algorithm predicts defect handling time with promising accuracy results.
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