@article{ji_jiang_li_fahid_chen_li_xiao_bao_zhu_2024, title={Learning to bid and rank together in recommendation systems( nov , 2023 , 10.1007/s10994-023-06444-4)}, ISSN={["1573-0565"]}, DOI={10.1007/s10994-023-06496-6}, journal={MACHINE LEARNING}, author={Ji, Geng and Jiang, Wentao and Li, Jiang and Fahid, Fahmid Morshed and Chen, Zhengxing and Li, Yinghua and Xiao, Jun and Bao, Chongxi and Zhu, Zheqing}, year={2024}, month={Jan} }
 @article{ji_jiang_li_fahid_chen_li_xiao_bao_zhu_2023, title={Learning to bid and rank together in recommendation systems}, ISSN={["1573-0565"]}, DOI={10.1007/s10994-023-06444-4}, abstractNote={AbstractMany Internet applications adopt real-time bidding mechanisms to ensure different services (types of content) are shown to the users through fair competitions. The service offering the highest bid price gets the content slot to present a list of items in its candidate pool. Through user interactions with the recommended items, the service obtains the desired engagement activities. We propose a contextual-bandit framework to jointly optimize the price to bid for the slot and the order to rank its candidates for a given service in this type of recommendation systems. Our method can take as input any feature that describes the user and the candidates, including the outputs of other machine learning models. We train  reinforcement learning policies using deep neural networks, and compute top-K Gaussian propensity scores to exclude the variance in the gradients caused by randomness unrelated to the reward. This setup further facilitates us to automatically find accurate reward functions that trade off between budget spending and user engagements. In online A/B experiments on two major services of Facebook Home Feed, Groups You Should Join and Friend Requests, our method statistically significantly boosted the number of groups joined by 14.7%, the number of friend requests accepted by 7.0%, and the number of daily active Facebook users by about 1 million, against strong hand-tuned baselines that have been iterated in production over years.}, journal={MACHINE LEARNING}, author={Ji, Geng and Jiang, Wentao and Li, Jiang and Fahid, Fahmid Morshed and Chen, Zhengxing and Li, Yinghua and Xiao, Jun and Bao, Chongxi and Zhu, Zheqing}, year={2023}, month={Nov} }
 @article{fahid_acosta_lee_carpenter_mott_bae_saleh_brush_glazewski_hmelo-silver_et al._2022, title={Multimodal Behavioral Disengagement Detection for Collaborative Game-Based Learning}, volume={13356}, ISBN={["978-3-031-11646-9"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-031-11647-6_38}, abstractNote={Collaborative game-based learning environments offer significant promise for creating effective and engaging group learning experiences. These environments enable small groups of students to work together toward a common goal by sharing information, asking questions, and constructing explanations. However, students periodically disengage from the learning process, which negatively affects their learning, and the impacts are more severe in collaborative learning environments as disengagement can propagate, affecting participation across the group. Here, we introduce a multimodal behavioral disengagement detection framework that uses facial expression analysis in conjunction with natural language analyses of group chat. We evaluate the framework with students interacting with a collaborative game-based learning environment for middle school science education. The multimodal behavioral disengagement detection framework integrating both facial expression and group chat modalities achieves higher levels of predictive accuracy than those of baseline unimodal models.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION: POSTERS AND LATE BREAKING RESULTS, WORKSHOPS AND TUTORIALS, INDUSTRY AND INNOVATION TRACKS, PRACTITIONERS AND DOCTORAL CONSORTIUM, PT II}, author={Fahid, Fahmid Morshed and Acosta, Halim and Lee, Seung and Carpenter, Dan and Mott, Bradford and Bae, Haesol and Saleh, Asmalina and Brush, Thomas and Glazewski, Krista and Hmelo-Silver, Cindy E. and et al.}, year={2022}, pages={218–221} }
 @article{fahid_rowe_spain_goldberg_pokorny_lester_2022, title={Robust Adaptive Scaffolding with Inverse Reinforcement Learning-Based Reward Design}, volume={13356}, ISBN={["978-3-031-11646-9"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-031-11647-6_35}, abstractNote={Reinforcement learning (RL) has shown significant potential for inducing data-driven scaffolding policies but designing reward functions that lead to effective policies is challenging. A promising solution is to use inverse RL to learn a reward function from effective demonstrations. This paper presents an inverse reward deep RL framework for inducing scaffolding policies in an adaptive learning environment. The framework centers on generating a data-driven model of immediate rewards by sampling high learning-gain episodes from previous student interactions and applying inverse RL. The resulting reward model is used to induce an adaptive scaffolding policy using batch constrained deep Q-learning. We evaluate this framework on data from 487 learners who completed an adaptive trianing course that provided direct instruction on principles of leading stability operations. Results show that the framework yields significantly better scaffolding policies more quickly compared to several RL baselines.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION: POSTERS AND LATE BREAKING RESULTS, WORKSHOPS AND TUTORIALS, INDUSTRY AND INNOVATION TRACKS, PRACTITIONERS AND DOCTORAL CONSORTIUM, PT II}, author={Fahid, Fahmid Morshed and Rowe, Jonathan P. and Spain, Randall D. and Goldberg, Benjamin S. and Pokorny, Robert and Lester, James}, year={2022}, pages={204–207} }
 @article{fahid_rowe_spain_goldberg_pokorny_lester_2021, title={Adaptively Scaffolding Cognitive Engagement with Batch Constrained Deep Q-Networks}, volume={12748}, ISBN={["978-3-030-78291-7"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-030-78292-4_10}, abstractNote={Scaffolding student engagement is a central challenge in adaptive learning environments. The ICAP framework defines levels of cognitive engagement with a learning activity in terms of four different engagement modes—Interactive, Constructive, Active, and Passive—and it predicts that increased cognitive engagement will yield improved learning. However, a key open question is how best to translate the ICAP theory into the design of adaptive scaffolding in adaptive learning environments. Specifically, should scaffolds be designed to require the highest levels of cognitive engagement (i.e., Interactive and Constructive modes) with every instance of feedback or knowledge component? To answer this question, in this paper we investigate a data-driven pedagogical modeling framework based on batch-constrained deep Q-networks, a type of deep reinforcement learning (RL) method, to induce policies for delivering ICAP-inspired scaffolding in adaptive learning environments. The policies are trained with log data from 487 learners as they interacted with an adaptive learning environment that provided ICAP-inspired feedback and remediation. Results suggest that adaptive scaffolding policies induced with batch-constrained deep Q-networks outperform heuristic policies that strictly follow the ICAP model without RL-based tailoring. The findings demonstrate the utility of deep RL for tailoring scaffolding for learner cognitive engagement.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT I}, author={Fahid, Fahmid Morshed and Rowe, Jonathan P. and Spain, Randall D. and Goldberg, Benjamin S. and Pokorny, Robert and Lester, James}, year={2021}, pages={113–124} }
 @article{shrikanth_nichols_fahid_menzies_2021, title={Assessing practitioner beliefs about software engineering}, volume={26}, ISSN={["1573-7616"]}, DOI={10.1007/s10664-021-09957-5}, abstractNote={Software engineering is a highly dynamic discipline. Hence, as times change, so too might our beliefs about core processes in this field. This paper checks some five beliefs that originated in the past decades that comment on the relationships between (i) developer productivity; (ii) software quality and (iii) years of developer experience. Using data collected from 1,356 developers in the period 1995 to 2006, we found support for only one of the five beliefs titled "Quality entails productivity". We found no clear support for four other beliefs based on programming languages and software developers. However, from the sporadic evidence of the four other beliefs we learned that a narrow scope could delude practitioners in misinterpreting certain effects to hold in their day to day work. Lastly, through an aggregated view of assessing the five beliefs, we find programming languages act as a confounding factor for developer productivity and software quality. Thus the overall message of this work is that it is both important and possible to revisit old beliefs in SE. Researchers and practitioners should routinely retest old beliefs.}, number={4}, journal={EMPIRICAL SOFTWARE ENGINEERING}, author={Shrikanth, N. C. and Nichols, William and Fahid, Fahmid Morshed and Menzies, Tim}, year={2021}, month={Jul} }
 @article{tian_wiggins_fahid_emerson_bounajim_smith_boyer_wiebe_mott_lester_2021, title={Modeling Frustration Trajectories and Problem-Solving Behaviors in Adaptive Learning Environments for Introductory Computer Science}, volume={12749}, ISBN={["978-3-030-78269-6"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-030-78270-2_63}, abstractNote={Modeling a learner's frustration in adaptive environments can inform scaffolding. While much work has explored momentary frustration, there is limited research investigating the dynamics of frustration over time and its relationship with problem-solving behaviors. In this paper, we clustered 86 undergraduate students into four frustration trajectories as they worked with an adaptive learning environment for introductory computer science. The results indicate that students who initially report high levels of frustration but then reported lower levels later in their problem solving were more likely to have sought help. These findings provide insight into how frustration trajectory models can guide adaptivity during extended problem-solving episodes.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT II}, author={Tian, Xiaoyi and Wiggins, Joseph B. and Fahid, Fahmid Morshed and Emerson, Andrew and Bounajim, Dolly and Smith, Andy and Boyer, Kristy Elizabeth and Wiebe, Eric and Mott, Bradford and Lester, James}, year={2021}, pages={355–360} }
 @article{yu_fahid_tu_menzies_2022, title={Identifying Self-Admitted Technical Debts With Jitterbug: A Two-Step Approach}, volume={48}, ISSN={["1939-3520"]}, url={https://doi.org/10.1109/TSE.2020.3031401}, DOI={10.1109/TSE.2020.3031401}, abstractNote={Keeping track of and managing Self-Admitted Technical Debts (SATDs) are important to maintaining a healthy software project. This requires much time and effort from human experts to identify the SATDs manually. The current automated solutions do not have satisfactory precision and recall in identifying SATDs to fully automate the process. To solve the above problems, we propose a two-step framework called Jitterbug for identifying SATDs. Jitterbug first identifies the “easy to find” SATDs automatically with close to 100 percent precision using a novel pattern recognition technique. Subsequently, machine learning techniques are applied to assist human experts in manually identifying the remaining “hard to find” SATDs with reduced human effort. Our simulation studies on ten software projects show that Jitterbug can identify SATDs more efficiently (with less human effort) than the prior state-of-the-art methods.}, number={5}, journal={IEEE TRANSACTIONS ON SOFTWARE ENGINEERING}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Yu, Zhe and Fahid, Fahmid Morshed and Tu, Huy and Menzies, Tim}, year={2022}, month={May}, pages={1676–1691} }
 @article{yu_fahid_menzies_rothermel_patrick_cherian_2019, title={TERMINATOR: Better Automated UI Test Case Prioritization}, DOI={10.1145/3338906.3340448}, abstractNote={Automated UI testing is an important component of the continuous integration process of software development. A modern web-based UI is an amalgam of reports from dozens of microservices written by multiple teams. Queries on a page that opens up another will fail if any of that page's microservices fails. As a result, the overall cost for automated UI testing is high since the UI elements cannot be tested in isolation. For example, the entire automated UI testing suite at LexisNexis takes around 30 hours (3-5 hours on the cloud) to execute, which slows down the continuous integration process. To mitigate this problem and give developers faster feedback on their code, test case prioritization techniques are used to reorder the automated UI test cases so that more failures can be detected earlier. Given that much of the automated UI testing is "black box" in nature, very little information (only the test case descriptions and testing results) can be utilized to prioritize these automated UI test cases. Hence, this paper evaluates 17 "black box" test case prioritization approaches that do not rely on source code information. Among these, we propose a novel TCP approach, that dynamically re-prioritizes the test cases when new failures are detected, by applying and adapting a state of the art framework from the total recall problem. Experimental results on LexisNexis automated UI testing data show that our new approach (which we call TERMINATOR), outperformed prior state of the art approaches in terms of failure detection rates with negligible CPU overhead.}, journal={ESEC/FSE'2019: PROCEEDINGS OF THE 2019 27TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING}, author={Yu, Zhe and Fahid, Fahmid and Menzies, Tim and Rothermel, Gregg and Patrick, Kyle and Cherian, Snehit}, year={2019}, pages={883–894} }