Learning Effective Changes for Software Projects

The primary motivation of much of software analytics is decision making. How to make these decisions? Should one make decisions based on lessons that arise from within a particular project? Or should one generate these decisions from across multiple projects? This work is an attempt to answer these questions. Our work was motivated by a realization that much of the current generation software analytics tools focus primarily on prediction. Indeed prediction is a useful task, but it is usually followed by "planning" about what actions need to be taken. This research seeks to address the planning task by seeking methods that support actionable analytics that offer clear guidance on what to do. Specifically, we propose XTREE and BELLTREE algorithms for generating a set of actionable plans within and across projects. Each of these plans, if followed will improve the quality of the software project.Comment: 4 pages, 2 figures. This a submission for ASE 2017 Doctoral Symposiu

FEMOSAA: Feature guided and knEe driven Multi-Objective optimization for Self-Adaptive softwAre

Self-Adaptive Software (SAS) can reconfigure itself to adapt to the changing environment at runtime, aiming to continually optimize conflicted nonfunctional objectives (e.g., response time, energy consumption, throughput, cost, etc.). In this article, we present Feature-guided and knEe-driven Multi-Objective optimization for Self-Adaptive softwAre (FEMOSAA), a novel framework that automatically synergizes the feature model and Multi-Objective Evolutionary Algorithm (MOEA) to optimize SAS at runtime. FEMOSAA operates in two phases: at design time, FEMOSAA automatically transposes the engineers’ design of SAS, expressed as a feature model, to fit the MOEA, creating new chromosome representation and reproduction operators. At runtime, FEMOSAA utilizes the feature model as domain knowledge to guide the search and further extend the MOEA, providing a larger chance for finding better solutions. In addition, we have designed a new method to search for the knee solutions, which can achieve a balanced tradeoff. We comprehensively evaluated FEMOSAA on two running SAS: One is a highly complex SAS with various adaptable real-world software under the realistic workload trace; another is a service-oriented SAS that can be dynamically composed from services. In particular, we compared the effectiveness and overhead of FEMOSAA against four of its variants and three other search-based frameworks for SAS under various scenarios, including three commonly applied MOEAs, two workload patterns, and diverse conflicting quality objectives. The results reveal the effectiveness of FEMOSAA and its superiority over the others with high statistical significance and nontrivial effect sizes

FEMOSAA: feature-guided and knee-driven multi-objective optimization for self-adaptive software

Self-Adaptive Software (SAS) can reconfigure itself to adapt to the changing environment at runtime, aiming to continually optimize conflicted nonfunctional objectives (e.g., response time, energy consumption, throughput, cost, etc.). In this article, we present Feature-guided and knEe-driven Multi-Objective optimization for Self-Adaptive softwAre (FEMOSAA), a novel framework that automatically synergizes the feature model and Multi-Objective Evolutionary Algorithm (MOEA) to optimize SAS at runtime. FEMOSAA operates in two phases: at design time, FEMOSAA automatically transposes the engineers’ design of SAS, expressed as a feature model, to fit the MOEA, creating new chromosome representation and reproduction operators. At runtime, FEMOSAA utilizes the feature model as domain knowledge to guide the search and further extend the MOEA, providing a larger chance for finding better solutions. In addition, we have designed a new method to search for the knee solutions, which can achieve a balanced tradeoff. We comprehensively evaluated FEMOSAA on two running SAS: One is a highly complex SAS with various adaptable real-world software under the realistic workload trace; another is a service-oriented SAS that can be dynamically composed from services. In particular, we compared the effectiveness and overhead of FEMOSAA against four of its variants and three other search-based frameworks for SAS under various scenarios, including three commonly applied MOEAs, two workload patterns, and diverse conflicting quality objectives. The results reveal the effectiveness of FEMOSAA and its superiority over the others with high statistical significance and nontrivial effect sizes

Extending the ‘Open-Closed Principle’ to automated algorithm configuration

Metaheuristics are an effective and diverse class of optimization algorithms: a means of obtaining solutions of acceptable quality for otherwise intractable problems. The selection, construction, and configuration of a metaheuristic for a given problem has historically been a manually intensive process based on experience, experimentation, and reasoning by metaphor. More recently, there has been interest in automating the process of algorithm configuration. In this paper, we identify shared state as an inhibitor of progress for such automation. To solve this problem, we introduce the Automated Open Closed Principle (AOCP), which stipulates design requirements for unintrusive reuse of algorithm frameworks and automated assembly of algorithms from an extensible palette of components. We demonstrate how the AOCP enables a greater degree of automation than previously possible via an example implementation.PostprintPeer reviewe

A Transdisciplinary Approach to Decision Support for Dams in the Northeastern U.S. with Hydropower Potential

The Federal Energy Regulatory Commission (FERC) is the regulatory body that oversees non-federally owned dam operations in the United States. With more than 300 hydropower dams across the U.S. seeking FERC relicense between 2020 and 2029, and 135 of those dams within the Northeast region alone, it is prudent to anticipate and plan for such decision-making processes. Anyone may be involved in FERC relicensing; in fact, FERC solicits public comment and requires the licensee to hold a public hearing during the process. Parties may also elect to apply for legal intervenor status, allowing them a more formal entry into the relicensing process. However, there are two key barriers that may keep the public from participating in a dam decision-making process in an impactful way. The first of these barriers is access to information. Having access to the types of information that matters to FERC is important, because it allows the participant to communicate their support or concerns about the relicensing using the language of the process. In particular, participants other than the licensee may not have access to project economic information, so this is a focus in my research. The second barrier is capacity to participate in a way that impacts the process (i.e., institutional knowledge about what kinds of decision criteria (factors) and decision alternatives (project options), as well as relevant data, that FERC typically weighs in their decision making or has considered in the past). Actors not privy to license information (perhaps encountering difficulty in navigating the FERC eLibrary), lacking knowledge of FERC process conventions, or otherwise unfamiliar with hydropower dam schemes or operations have substantial hurdles preventing their effective participation. My research, situated in the sustainability science arena, addresses hydropower project cost and performance assessment and multi-criteria considerations for dam decision support. I lead the development and assessment of an online Dam Decision Support Tool aimed at addressing barriers to the hydropower dam decision-making process. My work demonstrates possibilities for tailoring decision tools to incorporate stakeholder perspectives into decision making about hydropower dams

Advances in Computer Science and Engineering

The book Advances in Computer Science and Engineering constitutes the revised selection of 23 chapters written by scientists and researchers from all over the world. The chapters cover topics in the scientific fields of Applied Computing Techniques, Innovations in Mechanical Engineering, Electrical Engineering and Applications and Advances in Applied Modeling