With the proliferation of distributed energy resources (DERs) in the distribution grid, it is a c... more With the proliferation of distributed energy resources (DERs) in the distribution grid, it is a challenge to effectively control a large number of DERs resilient to the communication and security disruptions, as well as to provide the online grid services, such as voltage regulation and virtual power plant (VPP) dispatch. To this end, a hybrid feedback-based optimization algorithm along with deep learning forecasting technique is proposed to specifically address the cyber-related issues. The online decentralized feedback-based DER optimization control requires timely, accurate voltage measurement from the grid. However, in practice such information may not be received by the control center or even be corrupted. Therefore, the long short-term memory (LSTM) deep learning algorithm is employed to forecast delayed/missed/attacked messages with high accuracy. The IEEE 37-node feeder with high penetration of PV systems is used to validate the efficiency of the proposed hybrid algorithm. The results show that 1) the LSTM-forecasted lost voltage can effectively improve the performance of the DER control algorithm in the practical cyber-physical architecture; and 2) the LSTM forecasting strategy outperforms other strategies of using previous message and skipping dual parameter update.
Keystroke dynamics study the way in which users input text via their keyboards. Having the abilit... more Keystroke dynamics study the way in which users input text via their keyboards. Having the ability to differentiate users, typing behaviors can unobtrusively form a component of a behavioral biometric recognition system to improve existing account security. Keystroke dynamics systems on free-text data have previously required 500 or more characters to achieve reasonable performance. In this paper, we propose a novel instance-based graph comparison algorithm called the instance-based tail area density (ITAD) metric to reduce the number of keystrokes required to authenticate users. Additionally, commonly used features in the keystroke dynamics literature, such as monographs and digraphs, are all found to be useful in informing who is typing. The usefulness of these features for authentication is determined using a random forest classifier and validated across two publicly available datasets. Scores from the individual features are fused to form a single matching score. With the fused matching score and our ITAD metric, we achieve equal error rates (EERs) for 100 and 200 testing digraphs of 9.7% and 7.8% for the Clarkson II dataset, improving upon state-of-the-art of 35.3% and 15.3%.
Proceedings of the 7th International Conference on Information Systems Security and Privacy
Mobile devices typically rely on entry-point and other one-time authentication mechanisms such as... more Mobile devices typically rely on entry-point and other one-time authentication mechanisms such as a password, PIN, fingerprint, iris, or face. But these authentication types are prone to a wide attack vector and worse still, once compromised, fail to protect the user's account and data. In contrast, continuous authentication, based on traits of human behavior, can offer additional security measures in the device to authenticate against unauthorized users, even after the entry-point and one-time authentication has been compromised. To this end, we have collected a new data-set of multiple behavioral biometric modalities (49 users) when a user fills out an account recovery form in sitting using an Android app. These include motion events (acceleration and angular velocity), touch and swipe events, keystrokes, and pattern tracing. In this paper, we focus on authentication based on motion events by evaluating a set of score level fusion techniques to authenticate users based on the acceleration and angular velocity data. The best EERs of 2.4% and 6.9% for intra-and inter-session respectively, are achieved by fusing acceleration and angular velocity using Nandakumar et al.'s likelihood ratio (LR) based score fusion.
Utilization of the Internet in our everyday lives has made us vulnerable in terms of privacy and ... more Utilization of the Internet in our everyday lives has made us vulnerable in terms of privacy and security of our data and systems. Therefore, there is a pressing need to protect our data and systems by improving authentication mechanisms, which are expected to be low cost, unobtrusive, and ideally ubiquitous in nature. Behavioral biometric modalities such as mouse dynamics (mouse behaviors on a graphical user interface (GUI)) and widget interactions (another modality closely related to mouse dynamics that also considers the target (widget) of a GUI interaction, such as links, buttons, and combo-boxes) can bolster the security of existing authentication systems because of their ability to distinguish an individual based on their unique features. As a result, it can be difficult for an imposter to impersonate these behavioral biometrics, making them suitable for authentication. In this paper, we survey the literature on mouse dynamics and widget interactions dated from 1897 to 2023. We begin our survey with an account of the psychological perspectives on behavioral biometrics. We then analyze the literature along the following dimensions: tasks and experimental settings for data collection, taxonomy of raw attributes, feature extractions and mathematical definitions, publicly available datasets, algorithms (statistical, machine learning, and deep learning), data fusion, performance, and limitations. Lastly, we end the paper with presenting challenges and promising research opportunities.
Cyber Physical Grid-Interactive Distributed Energy Resources Control for VPP Dispatch and Regulation
2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe)
This paper presents a cyber-physical algorithm for grid-interactive Distributed Energy Resource (... more This paper presents a cyber-physical algorithm for grid-interactive Distributed Energy Resource (DER) control to enable two features of Virtual Power Plants (VPPs) dispatch and grid voltage regulation, considering the communication and security impacts. We first formulate the DER dispatch problem as a real-time, iterative, and grid-interactive DER control problem. Thereafter, we consider a probabilistic traffic model to characterize packet delays and loss in a communication network, and study how the delays enter the process of information exchange among the grid measurement units, local DER controllers and the grid control center that coordinately execute this dispatch algorithm. Finally, a strategy combining delay threshold and modified message update rules is proposed to immune the asynchrony resulting from the communications network traffic and it avoids possible numerical instabilities and sensitivities of the tracking and regulation capabilities of this DER control algorithm. By implementing the proposed cyber-physical algorithm on the modified IEEE 37-node system, our preliminary results exhibit that the uncertainties of the underlying communications infrastructure must be considered for the VPP tracking and regulation capabilities of any DER in a generic Cyber-Physical System (CPS), because the delayed voltage measurements in the uplink/bi-link cases result in the off-track in VPP dispatch and jittery in voltage regulation.
Shared Multi-Keyboard and Bilingual Datasets to Support Keystroke Dynamics Research
Proceedings of the Twelfth ACM Conference on Data and Application Security and Privacy
Keystroke dynamics has been shown to be a promising method for user authentication based on a use... more Keystroke dynamics has been shown to be a promising method for user authentication based on a user's typing rhythms. Over the years, it has seen increasing applications such as in preventing transaction fraud, account takeovers, and identity theft. However, due to the variable nature of keystroke dynamics, a user's typing patterns may vary on a different keyboard or in a different keyboard language setting, which may affect the system accuracy. In other words, an algorithm modeled with data collected using a mechanical keyboard may perform significantly differently when tested with an ergonomic keyboard. Similarly, an algorithm modeled with data collected in one language may perform significantly differently when tested with another language. Hence, there is a need to study the impact of multiple keyboards and multiple languages on keystroke dynamics performance. This motivated us to develop two free-text keystroke dynamics datasets. The first is a multi-keyboard keystroke dataset comprising of four (4) physical keyboards - mechanical, ergonomic, membrane, and laptop keyboards - and the second is a bilingual keystroke dataset in both English and Chinese languages. Data were collected from a total of 86 participants using a non-intrusive web-based keylogger in a semi-controlled setting. To the best of our knowledge, these are the first multi-keyboard and bilingual keystroke datasets, as well as the data collection software, to be made publicly available for research purposes. The usefulness of our datasets was demonstrated by evaluating the performance of two state-of-the-art free-text algorithms.
IEEE Transactions on Biometrics, Behavior, and Identity Science, 2020
Keystroke dynamics study the way in which users input text via their keyboards. Having the abilit... more Keystroke dynamics study the way in which users input text via their keyboards. Having the ability to differentiate users, typing behaviors can unobtrusively form a component of a behavioral biometric recognition system to improve existing account security. Keystroke dynamics systems on free-text data have previously required 500 or more characters to achieve reasonable performance. In this paper, we propose a novel instance-based graph comparison algorithm called the instance-based tail area density (ITAD) metric to reduce the number of keystrokes required to authenticate users. Additionally, commonly used features in the keystroke dynamics literature, such as monographs and digraphs, are all found to be useful in informing who is typing. The usefulness of these features for authentication is determined using a random forest classifier and validated across two publicly available datasets. Scores from the individual features are fused to form a single matching score. With the fused matching score and our ITAD metric, we achieve equal error rates (EERs) for 100 and 200 testing digraphs of 9.7% and 7.8% for the Clarkson II dataset, improving upon state-of-the-art of 35.3% and 15.3%.
Facts extracted from source code have been used to support a variety of software engineering acti... more Facts extracted from source code have been used to support a variety of software engineering activities, ranging from architectural understanding, through detection of design patterns, to program exploration. Several fact extractors have been developed and published in the literature, but most of them extract facts only from individual compilation units. Linking multiple fact-bases is largely overlooked. Source-level linkage is different from compilation linkage. Its goal is to assist a software engineer, not to produce an executable program. Thus a source-level linker needs to collect as many as possible facts that may be potentially helpful to a software engineer's task, many of which are not available from a compiler linker. We present the design of a source-level linker for C++. This linker has been used to analyze a dozen of Microsoft Foundation Classes (MFC) programs and over 200 C++ programs that cover an extensive subset of C++ features, including templates from the Standard Template Library (STL). As a further validation, we design a Structural Constraint Language, SCL, to express and machine-check a wide range of constraints on the Abstract Semantics Graph (ASG) produced by the linker.
Developers depend on APIs of frameworks and libraries to support the development process. Due to ... more Developers depend on APIs of frameworks and libraries to support the development process. Due to the large number of existing APIs, it is difficult to learn, remember, and use them during the development of a software. To mitigate the problem, modern integrated development environments provide code completion facilities that free developers from remembering every detail. In this paper, we introduce CSCC, a simple, efficient context-sensitive code completion tool that leverages previous code examples to support method completion. Compared to other existing code completion tools, CSCC uses new sources of contextual information together with lightweight source code analysis to better recommend API method calls.
Code completion helps developers use application programming interfaces (APIs) and frees them fro... more Code completion helps developers use application programming interfaces (APIs) and frees them from remembering every detail. In this paper, we first describe a novel technique called Context-sensitive Code Completion (CSCC) for improving the performance of API method call completion. CSCC is context sensitive in that it uses new sources of information as the context of a target method call. CSCC indexes method calls in code examples by their context. To recommend completion proposals, CSCC ranks candidate methods by the similarities between their contexts and the context of the target call. Evaluation using a set of subject systems and five popular state-of-the-art techniques suggests that CSCC performs better than existing type or example-based code completion systems. We conduct experiments to find how different contextual elements of the target call benefit CSCC. Next, we investigate the adaptability of the technique to support another form of code completion, i.e., field completion. Evaluation with eight different subject systems suggests that CSCC can easily support field completion with high accuracy. Finally, we compare CSCC with four popular statistical language models that support code completion. Results of statistical tests from our study suggest that CSCC not only outperforms those techniques that are based on token level language models, but also in most cases performs better or equally well with GraLan, the state-of-the-art graph-based language model.
Code Completion helps developers learn APIs and frees them from remembering every detail. In this... more Code Completion helps developers learn APIs and frees them from remembering every detail. In this paper, we describe a novel technique called CSCC (Context Sensitive Code Completion) for improving the performance of API method call completion. CSCC is context sensitive in that it uses new sources of information as the context of a target method call. CSCC indexes method calls in code examples by their contexts. To recommend completion proposals, CSCC ranks candidate methods by the similarities between their contexts and the context of the target call. Evaluation using a set of subject systems and five popular state-of-the-art techniques suggests that CSCC performs better than existing type or example-based code completion systems. We also investigate how the different contextual elements of the target call benefit CSCC.
Programmers copy and paste code for many reasons. Regardless of the specific reasons, similar cod... more Programmers copy and paste code for many reasons. Regardless of the specific reasons, similar code fragments (clones) are introduced into software systems. Like other software artifacts, clones may require attention and effort from programmers so that they can be understood, and correctly adapted and evolved. More specifically, when understanding and maintaining clones, programmers need to know where the clones are. Programmers also need to compare and contrast code clones in order to figure out how they correspond and differ. Finally, they also need to edit or remove clones. In addition to what clone detection-based tools can offer, more automated support is needed to better assist programmers in these activities. In this paper, we introduce a toolkit CnP that is aimed to support and manage clones proactively as they are created and evolved. We describe the initial features and the design decisions taken in CnP. We also discuss possible future design extensions.
Software frameworks enable developers to reuse existing well tested functionalities instead of ta... more Software frameworks enable developers to reuse existing well tested functionalities instead of taking the burden of implementing everything from scratch. However, to meet application specific requirements, the frameworks need to be customized via extension points. This is often done by passing a framework related object as an argument to an API call. To enable such customizations, the object can be created by extending a framework class, implementing an interface, or changing the properties of the object via API calls. However, it is both a common and non-trivial task to find all the details related to the customizations. In this paper, we present a tool, called FEMIR, that utilizes partial program analysis and graph mining technique to detect, group, and rank framework extension examples. The tool extends existing code completion infrastructure to inform developers about customization choices, enabling them to browse through extension points of a framework, and frequent usages of each point in terms of code examples. A video demo is made available at https://asaduzzamanparvez.wordpress.com/femir.
With the proliferation of distributed energy resources (DERs) in the distribution grid, it is a c... more With the proliferation of distributed energy resources (DERs) in the distribution grid, it is a challenge to effectively control a large number of DERs resilient to the communication and security disruptions, as well as to provide the online grid services, such as voltage regulation and virtual power plant (VPP) dispatch. To this end, a hybrid feedback-based optimization algorithm along with deep learning forecasting technique is proposed to specifically address the cyber-related issues. The online decentralized feedback-based DER optimization control requires timely, accurate voltage measurement from the grid. However, in practice such information may not be received by the control center or even be corrupted. Therefore, the long short-term memory (LSTM) deep learning algorithm is employed to forecast delayed/missed/attacked messages with high accuracy. The IEEE 37-node feeder with high penetration of PV systems is used to validate the efficiency of the proposed hybrid algorithm. The results show that 1) the LSTM-forecasted lost voltage can effectively improve the performance of the DER control algorithm in the practical cyber-physical architecture; and 2) the LSTM forecasting strategy outperforms other strategies of using previous message and skipping dual parameter update.
Keystroke dynamics study the way in which users input text via their keyboards. Having the abilit... more Keystroke dynamics study the way in which users input text via their keyboards. Having the ability to differentiate users, typing behaviors can unobtrusively form a component of a behavioral biometric recognition system to improve existing account security. Keystroke dynamics systems on free-text data have previously required 500 or more characters to achieve reasonable performance. In this paper, we propose a novel instance-based graph comparison algorithm called the instance-based tail area density (ITAD) metric to reduce the number of keystrokes required to authenticate users. Additionally, commonly used features in the keystroke dynamics literature, such as monographs and digraphs, are all found to be useful in informing who is typing. The usefulness of these features for authentication is determined using a random forest classifier and validated across two publicly available datasets. Scores from the individual features are fused to form a single matching score. With the fused matching score and our ITAD metric, we achieve equal error rates (EERs) for 100 and 200 testing digraphs of 9.7% and 7.8% for the Clarkson II dataset, improving upon state-of-the-art of 35.3% and 15.3%.
Proceedings of the 7th International Conference on Information Systems Security and Privacy
Mobile devices typically rely on entry-point and other one-time authentication mechanisms such as... more Mobile devices typically rely on entry-point and other one-time authentication mechanisms such as a password, PIN, fingerprint, iris, or face. But these authentication types are prone to a wide attack vector and worse still, once compromised, fail to protect the user's account and data. In contrast, continuous authentication, based on traits of human behavior, can offer additional security measures in the device to authenticate against unauthorized users, even after the entry-point and one-time authentication has been compromised. To this end, we have collected a new data-set of multiple behavioral biometric modalities (49 users) when a user fills out an account recovery form in sitting using an Android app. These include motion events (acceleration and angular velocity), touch and swipe events, keystrokes, and pattern tracing. In this paper, we focus on authentication based on motion events by evaluating a set of score level fusion techniques to authenticate users based on the acceleration and angular velocity data. The best EERs of 2.4% and 6.9% for intra-and inter-session respectively, are achieved by fusing acceleration and angular velocity using Nandakumar et al.'s likelihood ratio (LR) based score fusion.
Utilization of the Internet in our everyday lives has made us vulnerable in terms of privacy and ... more Utilization of the Internet in our everyday lives has made us vulnerable in terms of privacy and security of our data and systems. Therefore, there is a pressing need to protect our data and systems by improving authentication mechanisms, which are expected to be low cost, unobtrusive, and ideally ubiquitous in nature. Behavioral biometric modalities such as mouse dynamics (mouse behaviors on a graphical user interface (GUI)) and widget interactions (another modality closely related to mouse dynamics that also considers the target (widget) of a GUI interaction, such as links, buttons, and combo-boxes) can bolster the security of existing authentication systems because of their ability to distinguish an individual based on their unique features. As a result, it can be difficult for an imposter to impersonate these behavioral biometrics, making them suitable for authentication. In this paper, we survey the literature on mouse dynamics and widget interactions dated from 1897 to 2023. We begin our survey with an account of the psychological perspectives on behavioral biometrics. We then analyze the literature along the following dimensions: tasks and experimental settings for data collection, taxonomy of raw attributes, feature extractions and mathematical definitions, publicly available datasets, algorithms (statistical, machine learning, and deep learning), data fusion, performance, and limitations. Lastly, we end the paper with presenting challenges and promising research opportunities.
Cyber Physical Grid-Interactive Distributed Energy Resources Control for VPP Dispatch and Regulation
2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe)
This paper presents a cyber-physical algorithm for grid-interactive Distributed Energy Resource (... more This paper presents a cyber-physical algorithm for grid-interactive Distributed Energy Resource (DER) control to enable two features of Virtual Power Plants (VPPs) dispatch and grid voltage regulation, considering the communication and security impacts. We first formulate the DER dispatch problem as a real-time, iterative, and grid-interactive DER control problem. Thereafter, we consider a probabilistic traffic model to characterize packet delays and loss in a communication network, and study how the delays enter the process of information exchange among the grid measurement units, local DER controllers and the grid control center that coordinately execute this dispatch algorithm. Finally, a strategy combining delay threshold and modified message update rules is proposed to immune the asynchrony resulting from the communications network traffic and it avoids possible numerical instabilities and sensitivities of the tracking and regulation capabilities of this DER control algorithm. By implementing the proposed cyber-physical algorithm on the modified IEEE 37-node system, our preliminary results exhibit that the uncertainties of the underlying communications infrastructure must be considered for the VPP tracking and regulation capabilities of any DER in a generic Cyber-Physical System (CPS), because the delayed voltage measurements in the uplink/bi-link cases result in the off-track in VPP dispatch and jittery in voltage regulation.
Shared Multi-Keyboard and Bilingual Datasets to Support Keystroke Dynamics Research
Proceedings of the Twelfth ACM Conference on Data and Application Security and Privacy
Keystroke dynamics has been shown to be a promising method for user authentication based on a use... more Keystroke dynamics has been shown to be a promising method for user authentication based on a user's typing rhythms. Over the years, it has seen increasing applications such as in preventing transaction fraud, account takeovers, and identity theft. However, due to the variable nature of keystroke dynamics, a user's typing patterns may vary on a different keyboard or in a different keyboard language setting, which may affect the system accuracy. In other words, an algorithm modeled with data collected using a mechanical keyboard may perform significantly differently when tested with an ergonomic keyboard. Similarly, an algorithm modeled with data collected in one language may perform significantly differently when tested with another language. Hence, there is a need to study the impact of multiple keyboards and multiple languages on keystroke dynamics performance. This motivated us to develop two free-text keystroke dynamics datasets. The first is a multi-keyboard keystroke dataset comprising of four (4) physical keyboards - mechanical, ergonomic, membrane, and laptop keyboards - and the second is a bilingual keystroke dataset in both English and Chinese languages. Data were collected from a total of 86 participants using a non-intrusive web-based keylogger in a semi-controlled setting. To the best of our knowledge, these are the first multi-keyboard and bilingual keystroke datasets, as well as the data collection software, to be made publicly available for research purposes. The usefulness of our datasets was demonstrated by evaluating the performance of two state-of-the-art free-text algorithms.
IEEE Transactions on Biometrics, Behavior, and Identity Science, 2020
Keystroke dynamics study the way in which users input text via their keyboards. Having the abilit... more Keystroke dynamics study the way in which users input text via their keyboards. Having the ability to differentiate users, typing behaviors can unobtrusively form a component of a behavioral biometric recognition system to improve existing account security. Keystroke dynamics systems on free-text data have previously required 500 or more characters to achieve reasonable performance. In this paper, we propose a novel instance-based graph comparison algorithm called the instance-based tail area density (ITAD) metric to reduce the number of keystrokes required to authenticate users. Additionally, commonly used features in the keystroke dynamics literature, such as monographs and digraphs, are all found to be useful in informing who is typing. The usefulness of these features for authentication is determined using a random forest classifier and validated across two publicly available datasets. Scores from the individual features are fused to form a single matching score. With the fused matching score and our ITAD metric, we achieve equal error rates (EERs) for 100 and 200 testing digraphs of 9.7% and 7.8% for the Clarkson II dataset, improving upon state-of-the-art of 35.3% and 15.3%.
Facts extracted from source code have been used to support a variety of software engineering acti... more Facts extracted from source code have been used to support a variety of software engineering activities, ranging from architectural understanding, through detection of design patterns, to program exploration. Several fact extractors have been developed and published in the literature, but most of them extract facts only from individual compilation units. Linking multiple fact-bases is largely overlooked. Source-level linkage is different from compilation linkage. Its goal is to assist a software engineer, not to produce an executable program. Thus a source-level linker needs to collect as many as possible facts that may be potentially helpful to a software engineer's task, many of which are not available from a compiler linker. We present the design of a source-level linker for C++. This linker has been used to analyze a dozen of Microsoft Foundation Classes (MFC) programs and over 200 C++ programs that cover an extensive subset of C++ features, including templates from the Standard Template Library (STL). As a further validation, we design a Structural Constraint Language, SCL, to express and machine-check a wide range of constraints on the Abstract Semantics Graph (ASG) produced by the linker.
Developers depend on APIs of frameworks and libraries to support the development process. Due to ... more Developers depend on APIs of frameworks and libraries to support the development process. Due to the large number of existing APIs, it is difficult to learn, remember, and use them during the development of a software. To mitigate the problem, modern integrated development environments provide code completion facilities that free developers from remembering every detail. In this paper, we introduce CSCC, a simple, efficient context-sensitive code completion tool that leverages previous code examples to support method completion. Compared to other existing code completion tools, CSCC uses new sources of contextual information together with lightweight source code analysis to better recommend API method calls.
Code completion helps developers use application programming interfaces (APIs) and frees them fro... more Code completion helps developers use application programming interfaces (APIs) and frees them from remembering every detail. In this paper, we first describe a novel technique called Context-sensitive Code Completion (CSCC) for improving the performance of API method call completion. CSCC is context sensitive in that it uses new sources of information as the context of a target method call. CSCC indexes method calls in code examples by their context. To recommend completion proposals, CSCC ranks candidate methods by the similarities between their contexts and the context of the target call. Evaluation using a set of subject systems and five popular state-of-the-art techniques suggests that CSCC performs better than existing type or example-based code completion systems. We conduct experiments to find how different contextual elements of the target call benefit CSCC. Next, we investigate the adaptability of the technique to support another form of code completion, i.e., field completion. Evaluation with eight different subject systems suggests that CSCC can easily support field completion with high accuracy. Finally, we compare CSCC with four popular statistical language models that support code completion. Results of statistical tests from our study suggest that CSCC not only outperforms those techniques that are based on token level language models, but also in most cases performs better or equally well with GraLan, the state-of-the-art graph-based language model.
Code Completion helps developers learn APIs and frees them from remembering every detail. In this... more Code Completion helps developers learn APIs and frees them from remembering every detail. In this paper, we describe a novel technique called CSCC (Context Sensitive Code Completion) for improving the performance of API method call completion. CSCC is context sensitive in that it uses new sources of information as the context of a target method call. CSCC indexes method calls in code examples by their contexts. To recommend completion proposals, CSCC ranks candidate methods by the similarities between their contexts and the context of the target call. Evaluation using a set of subject systems and five popular state-of-the-art techniques suggests that CSCC performs better than existing type or example-based code completion systems. We also investigate how the different contextual elements of the target call benefit CSCC.
Programmers copy and paste code for many reasons. Regardless of the specific reasons, similar cod... more Programmers copy and paste code for many reasons. Regardless of the specific reasons, similar code fragments (clones) are introduced into software systems. Like other software artifacts, clones may require attention and effort from programmers so that they can be understood, and correctly adapted and evolved. More specifically, when understanding and maintaining clones, programmers need to know where the clones are. Programmers also need to compare and contrast code clones in order to figure out how they correspond and differ. Finally, they also need to edit or remove clones. In addition to what clone detection-based tools can offer, more automated support is needed to better assist programmers in these activities. In this paper, we introduce a toolkit CnP that is aimed to support and manage clones proactively as they are created and evolved. We describe the initial features and the design decisions taken in CnP. We also discuss possible future design extensions.
Software frameworks enable developers to reuse existing well tested functionalities instead of ta... more Software frameworks enable developers to reuse existing well tested functionalities instead of taking the burden of implementing everything from scratch. However, to meet application specific requirements, the frameworks need to be customized via extension points. This is often done by passing a framework related object as an argument to an API call. To enable such customizations, the object can be created by extending a framework class, implementing an interface, or changing the properties of the object via API calls. However, it is both a common and non-trivial task to find all the details related to the customizations. In this paper, we present a tool, called FEMIR, that utilizes partial program analysis and graph mining technique to detect, group, and rank framework extension examples. The tool extends existing code completion infrastructure to inform developers about customization choices, enabling them to browse through extension points of a framework, and frequent usages of each point in terms of code examples. A video demo is made available at https://asaduzzamanparvez.wordpress.com/femir.
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Papers by Daqing Hou