In this paper we focus on 112 functions from the Python pandas library for DataFrame manipulation , an order of magnitude more than considered in prior approaches . To assess the viability of program synthesis in this domain , our first goal is a system that reliably synthesizes programs with a single library function . We introduce an encoding of structured input – output examples as graphs that can be fed to existing graph-based neural networks to infer the library function . Recent advances in program synthesis convinced us that it was the right time to transform the process of porting an operating system into a program synthesis problem . We set out to synthesize the needed machine dependent code for an existing operating system . The emphasis of the work has been on automating as much as possible of the program derivation process . Theorem-proving methods particularly well-suited to the program synthesis application have been developed . An interactive program-derivation system has been implemented . We show how the user of our system is involved in solving a program synthesis problem . We show that this interaction does not concern the problem of guiding the program synthesis process , this being solved by CM . Our experimental version serves to confirm that the system is worth to be developed . In the previous chapter , we identified evaluation bottleneck , and showed that the information lost in aggregation of interaction outcomes can be essential for the success of a program synthesis process . In this chapter , we set out to present behavioral program synthesis , a new paradigm of program behavioral program synthesis which , in a sense , puts program behavior before its source code . We are trying to address this issue by developing the CRM system using GME through meta-model approach . GME is a configurable toolset that supports the easy creation of domain specific modeling and program synthesis environment . Enterprises of all descriptions have long sought to integrate and manage all of their information assets within a single system . A Taxonomy of “ simple ” ACTs has been created from initial , informal studies of system/user interaction . A knowledge-based synthesis approach is used to create an experimental environment to support a program synthesis ( software ) project [ Phillips-81 ] . The environment design and framework , which is part of the author 's Ph.D. thesis work in progress [ Kedzierski-80 ] , is discussed . A brief overview of known approaches to programme verification is presented , in addition some problems are analyzed and suggestions for improving the results of education in programming through using formal methods are discussed . The method for programme synthesis under discussion is based on a special function called weakest precondition . It was adapted according to the goals of education in programming based on C++ . The expanding processor – memory gap encourages developers to find aggressive techniques to reduce the latency of memory accesses . Value prediction is a powerful approach to break true data dependencies . Prefetching is another technique , which aims to reduce the processor stall time by bringing data into the cache before it is accessed by the processor . For each cluster , a medication feature is constructed and added to the data for the final feature selection . Although it was expected that clustering patients based on comorbidity data could improve the results of value prediction , the results were not improved in average . The reason for this could be the small number of abnormal laboratory test samples and their dispersion in clusters . The effects of interference between past data on perceptrons are evaluated , and different interference reduction strategies are explored . Perceptrons are then applied to two speculative applications : data value prediction and dataflow critical path prediction . Several new perceptron value predictors are proposed that can consider longer or more varied data histories than existing table-based value predictors . A first evaluation of the model was done by using the generated mean motion model to predict lung and tumor motion of individual patients and comparing the prediction quality to non - linear registration . Our results show that the average difference in prediction quality ( measured by overlap coefficients ) between non - linear registration and model - based prediction is approx . 10 % . The purpose of this work is to analyze some value-based branch predictors presented in the scientific literature and evaluate their accuracy and performance . The report is organized as follows : the first chapter introduces control hazards and value-based prediction through the literature . The second and the third chapters focus on the software implementation of the chosen predictor , first statically - through statistics - then dynamically - modifying the execution flow . Combining these findings with previous results , we conclude that : ( i ) carefully constructed , physically intuitive proxy parameters may be our best asset toward an efficient future flare-forecasting ; and ( ii ) the time series of promising parameters may be as important as their instantaneous values . Value-based prediction is the only approach followed so far . Our results call for novel signal and/or image processing techniques to efficiently utilize combined amplitude and temporal-profile information to optimize the inferred solar-flare probabilities . A Markov reward process model is introduced for modeling the behaviors of system call sequences and the intrusion detection problem is converted to predicting the value functions of the Markov reward process . A temporal different learning algorithm using linear basis functions is used for value function prediction so that abnormal temporal behaviors of host processes can be predicted accurately and efficiently . The proposed method has advantages over previous algorithms in that the temporal property of system call data is well captured in a natural and simple way and better intrusion detection performance can be achieved . In value-based reinforcement learning ( RL ) , unlike in supervised learning , the agent faces not a single , stationary , approximation problem , but a sequence of value prediction problems . Each time the policy improves , the nature of the problem changes , shifting both the distribution of states and their values . In this paper we take a novel perspective , arguing that the value prediction problems faced by an RL agent should not be addressed in isolation , but rather as a single , holistic , prediction problem . An RL algorithm generates a sequence of policies that , at least approximately , improve towards the optimal policy . Probabilistic neural networks implement a special class of multivariate non-linear Bayesian statistical models . The mathematical principles supporting their use for value prediction purposes are clarified and their peculiarities discussed . As part of the research phase of the data mining process , a dataset consisting of 800 structures and associated fathead minnow ( Pimephales promelas ) Standard value - based RL algorithms like SARSA and OLPOMDP have also been applied to structured prediction maes2009structured . Again , these systems do not use the ground - truth for value prediction . Imitation learning has also been applied to structured prediction vlachos2012investigation . Thus a natural question to ask is when the evaluation is independent of the context . Such information has immediate applications in optimization of DL-programs , but is also beneficial for other reasoning tasks , like inconsistency diagnosis and program repair . We provide an answer to this question based on a semantic notion of independence and provide a complete characterization of independent DL-atoms . The number of tests is bounded using evolution constraints , which restrict the forms the software may take as it evolves . The techniques presented here can yield pessimistic results during program repair because they make few assumptions about the repair process . They are most appropriate when applied to software whose initial development is already completed . [ Aim ] : The detection and characterization of common bug-fix patterns in software repositories play an important role in advancing the field of automatic program repair . In this paper , we aim to characterize the occurrence of known bug-fix patterns in Java repositories at an unprecedented large scale . We show that these rules can be automated by using an off-the-shelf Horn constraint solver that supports existential quantification in clause heads . The practical promise of the rules is demonstrated through several case studies , including a challenging " Cinderella-Stepmother game " that allows infinite alternation of discrete and continuous choices by two players , as well as examples derived from prior work on program repair and synthesis . An empirical study of 447 operational commercial and clerical Cobol programs in one Australian organization and two U.S. organizations was carried out to determine whether program complexity , programming style , programmer quality , and the number of times a program was released affected program repair maintenance . In the Australian organization only program complexity and programming style were statistically significant . In this work , we introduce SmartPaste , a first task that requires to use such information . The task is a variant of the program repair problem that requires to adapt a given ( pasted ) snippet of code to surrounding , existing source code . As first solutions , we design a set of deep neural models that learn to represent the context of each variable location and variable usage in a data flow-sensitive way . Our system is data-driven ; we train it on 35,578 samples , carefully curated from commits to open-source repositories . We evaluate SEQUENCER on 4,711 independent real bug fixes , as well on the Defects4J benchmark used in program repair research . SEQUENCER is able to perfectly predict the fixed line for 950/4,711 testing samples , and find correct patches for 14 bugs in Defects4J benchmark . We present GradeIT , a system that combines the dual objectives of automated grading and program repairing for introductory programming courses ( CS1 ) . Syntax errors pose a significant challenge for testcase-based grading as it is difficult to differentiate between a submission that is almost correct and has some minor syntax errors and another submission that is completely off-the-mark . The key idea is to reduce the problem from 2QBF to SAT by sampling the space of inputs , which eliminates the universal quantification over x. Finally , we show how to encode the resulting SAT problem in relational logic , and how this encoding can be used to solve a range of related problems that arise in synthesis , from verification to program state repair . We will conclude with open problems on constraint-based synthesis . More work is needed to advance the capabilities of modern APR techniques . We propose a new technique that utilizes the wealth of bug fixesacross projects in their development history to effectively guide and drive a programrepair process . Our main insight is that recurring bug fixes are common inreal-world applications , and that previously-appearing fix patterns canprovide useful guidance to an automated repair technique . We evaluate the proposed approach in a number of random generated example models from various domains . The correct type prediction varies from 23 to 100 % depending on the domain , the proportion of elements that were left untyped and the prediction algorithm used . The suitability of the proposal is demonstrated in an empirical study done with ten widely used microarray datasets , and the results show its effectiveness and competitiveness compared with four state-of-the-art methods . The experimental results on St Jude dataset shows that our method can be effectively applied to microarray data analysis for subtype prediction and the discovery of gene coexpression . The resulting image features capture some relevant tumor morphology patterns and led to a classifier performing similarly to one constructed from features annotated by an expert pathologist . The significance of our results extends beyond subtype prediction since they demonstrate a possible approach to multimodal ( histopathology and molecular ) data mining and biomarker identification . Automatic type prediction can help reduce such incompleteness , and , as previous works show , statistical methods are well-suited for this kind of data . Since most Semantic Web knowledge bases come with an ontology defining a type hierarchy , in this paper , we rephrase the type prediction problem as a hierarchical multilabel classification problem . We propose SLCN , a modification of the local classifier per node approach , which performs feature selection , instance sampling , and class balancing for each local classifier . This study uses two artificial neural networks ( ANNs ) , categorical learning/instar ANNs and probabilistic ( PNN ) ANNs , suitable for classification and prediction type issues , and compares them to traditional multivariate discriminant analysis ( MDA ) and logit to examine financial distress one to three years prior to failure . The results indicate that traditional MDA and logit perform best with the lowest overall error rates . The usefulness of tabular data such as web tables critically depends on understanding their semantics . This study focuses on column type prediction for tables without any meta data . Unlike traditional lexical matching-based methods , we propose a deep prediction model that can fully exploit a table 's contextual semantics , including table locality features learned by a Hybrid Neural Network ( HNN ) , and inter-column semantics features learned by a knowledge base ( KB ) lookup and query answering this http URL exhibits good performance not only on individual table sets , but also when transferring from one table set to another . We present a dataset of manually annotated relationships between characters in literary texts , in order to support the training and evaluation of automatic methods for relation type prediction in this domain ( Makazhanov et al. , 2014 ; Kokkinakis , 2013 ) and the broader computational analysis of literary character ( Elson et al. , 2010 ; Bamman et al. , 2014 ; Vala et al. , 2015 ; Flekova and Gurevych , 2015 ) . In this work , we solicit annotations from workers on Amazon Mechanical Turk for 109 texts ranging from Homer 's _ Iliad _ to Joyce 's _ Ulysses _ on four dimensions of interest : for a given pair of characters , we collect judgments as to the coarse-grained category ( professional , social , familial ) , fine-grained category ( friend , lover , parent , rival , employer ) , and affinity ( positive , negative , neutral ) that describes their primary relationship in a text . This dissertation focuses on addressing this problem : we present new techniques that learn probabilistic models from large datasets of programs as well as new tools based on these probabilistic models which improve software development . The thesis presents three new software systems ( JSNice , Slang and DeepSyn ) that learn from large datasets of programs and provide likely solutions to previously unsolved programming tasks including deobfuscation , static type prediction for dynamic languages , and code synthesis . All three of these systems were trained on thousands of open source projects and answer real-world queries in seconds and with high precision . Our proposed supervised and unsupervised approaches perform better than the supervised and unsupervised approaches of Fazly et al. ( 2009 ) , respectively . 1 Verb – noun Idiomatic Combinations Much research on multiword expressions ( MWEs ) in natural language processing ( NLP ) has focused on various type-level prediction tasks , e.g. , MWE extraction ( e.g. , Church and Hanks , 1990 ; Smadja , 1993 ; Lin , 1999 ) — i.e. , determining which MWE types are present in a given corpus ( Baldwin and Kim , 2010 ) — and compositionality prediction ( e.g. , McCarthy et al. , 2003 ; Reddy et al. , 2011 ; Salehi et al. , 2014 ) . However , word combinations can be ambiguous between literal combinations and MWEs .
In this paper we focus on 112 functions from the Python pandas library for DataFrame manipulation , an order of magnitude more than considered in prior approaches . To assess the viability of program synthesis in this domain , our first goal is a system that reliably synthesizes programs with a single library function . We introduce an encoding of structured input – output examples as graphs that can be fed to existing graph-based neural networks to infer the library function .
Recent advances in program synthesis convinced us that it was the right time to transform the process of porting an operating system into a program synthesis problem . We set out to synthesize the needed machine dependent code for an existing operating system .
The emphasis of the work has been on automating as much as possible of the program derivation process . Theorem-proving methods particularly well-suited to the program synthesis application have been developed . An interactive program-derivation system has been implemented .
We show how the user of our system is involved in solving a program synthesis problem . We show that this interaction does not concern the problem of guiding the program synthesis process , this being solved by CM . Our experimental version serves to confirm that the system is worth to be developed .
In the previous chapter , we identified evaluation bottleneck , and showed that the information lost in aggregation of interaction outcomes can be essential for the success of a program synthesis process . In this chapter , we set out to present behavioral program synthesis , a new paradigm of program behavioral program synthesis which , in a sense , puts program behavior before its source code .
We are trying to address this issue by developing the CRM system using GME through meta-model approach . GME is a configurable toolset that supports the easy creation of domain specific modeling and program synthesis environment . Enterprises of all descriptions have long sought to integrate and manage all of their information assets within a single system .
A Taxonomy of “ simple ” ACTs has been created from initial , informal studies of system/user interaction . A knowledge-based synthesis approach is used to create an experimental environment to support a program synthesis ( software ) project [ Phillips-81 ] . The environment design and framework , which is part of the author 's Ph.D. thesis work in progress [ Kedzierski-80 ] , is discussed .
A brief overview of known approaches to programme verification is presented , in addition some problems are analyzed and suggestions for improving the results of education in programming through using formal methods are discussed . The method for programme synthesis under discussion is based on a special function called weakest precondition . It was adapted according to the goals of education in programming based on C++ .
The expanding processor – memory gap encourages developers to find aggressive techniques to reduce the latency of memory accesses . Value prediction is a powerful approach to break true data dependencies . Prefetching is another technique , which aims to reduce the processor stall time by bringing data into the cache before it is accessed by the processor .
For each cluster , a medication feature is constructed and added to the data for the final feature selection . Although it was expected that clustering patients based on comorbidity data could improve the results of value prediction , the results were not improved in average . The reason for this could be the small number of abnormal laboratory test samples and their dispersion in clusters .
The effects of interference between past data on perceptrons are evaluated , and different interference reduction strategies are explored . Perceptrons are then applied to two speculative applications : data value prediction and dataflow critical path prediction . Several new perceptron value predictors are proposed that can consider longer or more varied data histories than existing table-based value predictors .
A first evaluation of the model was done by using the generated mean motion model to predict lung and tumor motion of individual patients and comparing the prediction quality to non - linear registration . Our results show that the average difference in prediction quality ( measured by overlap coefficients ) between non - linear registration and model - based prediction is approx . 10 % .
The purpose of this work is to analyze some value-based branch predictors presented in the scientific literature and evaluate their accuracy and performance . The report is organized as follows : the first chapter introduces control hazards and value-based prediction through the literature . The second and the third chapters focus on the software implementation of the chosen predictor , first statically - through statistics - then dynamically - modifying the execution flow .
Combining these findings with previous results , we conclude that : ( i ) carefully constructed , physically intuitive proxy parameters may be our best asset toward an efficient future flare-forecasting ; and ( ii ) the time series of promising parameters may be as important as their instantaneous values . Value-based prediction is the only approach followed so far . Our results call for novel signal and/or image processing techniques to efficiently utilize combined amplitude and temporal-profile information to optimize the inferred solar-flare probabilities .
A Markov reward process model is introduced for modeling the behaviors of system call sequences and the intrusion detection problem is converted to predicting the value functions of the Markov reward process . A temporal different learning algorithm using linear basis functions is used for value function prediction so that abnormal temporal behaviors of host processes can be predicted accurately and efficiently . The proposed method has advantages over previous algorithms in that the temporal property of system call data is well captured in a natural and simple way and better intrusion detection performance can be achieved .
In value-based reinforcement learning ( RL ) , unlike in supervised learning , the agent faces not a single , stationary , approximation problem , but a sequence of value prediction problems . Each time the policy improves , the nature of the problem changes , shifting both the distribution of states and their values . In this paper we take a novel perspective , arguing that the value prediction problems faced by an RL agent should not be addressed in isolation , but rather as a single , holistic , prediction problem . An RL algorithm generates a sequence of policies that , at least approximately , improve towards the optimal policy .
Probabilistic neural networks implement a special class of multivariate non-linear Bayesian statistical models . The mathematical principles supporting their use for value prediction purposes are clarified and their peculiarities discussed . As part of the research phase of the data mining process , a dataset consisting of 800 structures and associated fathead minnow ( Pimephales promelas )
Standard value - based RL algorithms like SARSA and OLPOMDP have also been applied to structured prediction maes2009structured . Again , these systems do not use the ground - truth for value prediction . Imitation learning has also been applied to structured prediction vlachos2012investigation .
Thus a natural question to ask is when the evaluation is independent of the context . Such information has immediate applications in optimization of DL-programs , but is also beneficial for other reasoning tasks , like inconsistency diagnosis and program repair . We provide an answer to this question based on a semantic notion of independence and provide a complete characterization of independent DL-atoms .
The number of tests is bounded using evolution constraints , which restrict the forms the software may take as it evolves . The techniques presented here can yield pessimistic results during program repair because they make few assumptions about the repair process . They are most appropriate when applied to software whose initial development is already completed .
[ Aim ] : The detection and characterization of common bug-fix patterns in software repositories play an important role in advancing the field of automatic program repair . In this paper , we aim to characterize the occurrence of known bug-fix patterns in Java repositories at an unprecedented large scale .
We show that these rules can be automated by using an off-the-shelf Horn constraint solver that supports existential quantification in clause heads . The practical promise of the rules is demonstrated through several case studies , including a challenging " Cinderella-Stepmother game " that allows infinite alternation of discrete and continuous choices by two players , as well as examples derived from prior work on program repair and synthesis .
An empirical study of 447 operational commercial and clerical Cobol programs in one Australian organization and two U.S. organizations was carried out to determine whether program complexity , programming style , programmer quality , and the number of times a program was released affected program repair maintenance . In the Australian organization only program complexity and programming style were statistically significant .
In this work , we introduce SmartPaste , a first task that requires to use such information . The task is a variant of the program repair problem that requires to adapt a given ( pasted ) snippet of code to surrounding , existing source code . As first solutions , we design a set of deep neural models that learn to represent the context of each variable location and variable usage in a data flow-sensitive way .
Our system is data-driven ; we train it on 35,578 samples , carefully curated from commits to open-source repositories . We evaluate SEQUENCER on 4,711 independent real bug fixes , as well on the Defects4J benchmark used in program repair research . SEQUENCER is able to perfectly predict the fixed line for 950/4,711 testing samples , and find correct patches for 14 bugs in Defects4J benchmark .
We present GradeIT , a system that combines the dual objectives of automated grading and program repairing for introductory programming courses ( CS1 ) . Syntax errors pose a significant challenge for testcase-based grading as it is difficult to differentiate between a submission that is almost correct and has some minor syntax errors and another submission that is completely off-the-mark .
The key idea is to reduce the problem from 2QBF to SAT by sampling the space of inputs , which eliminates the universal quantification over x. Finally , we show how to encode the resulting SAT problem in relational logic , and how this encoding can be used to solve a range of related problems that arise in synthesis , from verification to program state repair . We will conclude with open problems on constraint-based synthesis .
More work is needed to advance the capabilities of modern APR techniques . We propose a new technique that utilizes the wealth of bug fixesacross projects in their development history to effectively guide and drive a programrepair process . Our main insight is that recurring bug fixes are common inreal-world applications , and that previously-appearing fix patterns canprovide useful guidance to an automated repair technique .
We evaluate the proposed approach in a number of random generated example models from various domains . The correct type prediction varies from 23 to 100 % depending on the domain , the proportion of elements that were left untyped and the prediction algorithm used .
The suitability of the proposal is demonstrated in an empirical study done with ten widely used microarray datasets , and the results show its effectiveness and competitiveness compared with four state-of-the-art methods . The experimental results on St Jude dataset shows that our method can be effectively applied to microarray data analysis for subtype prediction and the discovery of gene coexpression .
The resulting image features capture some relevant tumor morphology patterns and led to a classifier performing similarly to one constructed from features annotated by an expert pathologist . The significance of our results extends beyond subtype prediction since they demonstrate a possible approach to multimodal ( histopathology and molecular ) data mining and biomarker identification .
Automatic type prediction can help reduce such incompleteness , and , as previous works show , statistical methods are well-suited for this kind of data . Since most Semantic Web knowledge bases come with an ontology defining a type hierarchy , in this paper , we rephrase the type prediction problem as a hierarchical multilabel classification problem . We propose SLCN , a modification of the local classifier per node approach , which performs feature selection , instance sampling , and class balancing for each local classifier .
This study uses two artificial neural networks ( ANNs ) , categorical learning/instar ANNs and probabilistic ( PNN ) ANNs , suitable for classification and prediction type issues , and compares them to traditional multivariate discriminant analysis ( MDA ) and logit to examine financial distress one to three years prior to failure . The results indicate that traditional MDA and logit perform best with the lowest overall error rates .
The usefulness of tabular data such as web tables critically depends on understanding their semantics . This study focuses on column type prediction for tables without any meta data . Unlike traditional lexical matching-based methods , we propose a deep prediction model that can fully exploit a table 's contextual semantics , including table locality features learned by a Hybrid Neural Network ( HNN ) , and inter-column semantics features learned by a knowledge base ( KB ) lookup and query answering this http URL exhibits good performance not only on individual table sets , but also when transferring from one table set to another .
We present a dataset of manually annotated relationships between characters in literary texts , in order to support the training and evaluation of automatic methods for relation type prediction in this domain ( Makazhanov et al. , 2014 ; Kokkinakis , 2013 ) and the broader computational analysis of literary character ( Elson et al. , 2010 ; Bamman et al. , 2014 ; Vala et al. , 2015 ; Flekova and Gurevych , 2015 ) . In this work , we solicit annotations from workers on Amazon Mechanical Turk for 109 texts ranging from Homer 's _ Iliad _ to Joyce 's _ Ulysses _ on four dimensions of interest : for a given pair of characters , we collect judgments as to the coarse-grained category ( professional , social , familial ) , fine-grained category ( friend , lover , parent , rival , employer ) , and affinity ( positive , negative , neutral ) that describes their primary relationship in a text .
This dissertation focuses on addressing this problem : we present new techniques that learn probabilistic models from large datasets of programs as well as new tools based on these probabilistic models which improve software development . The thesis presents three new software systems ( JSNice , Slang and DeepSyn ) that learn from large datasets of programs and provide likely solutions to previously unsolved programming tasks including deobfuscation , static type prediction for dynamic languages , and code synthesis . All three of these systems were trained on thousands of open source projects and answer real-world queries in seconds and with high precision .
Our proposed supervised and unsupervised approaches perform better than the supervised and unsupervised approaches of Fazly et al. ( 2009 ) , respectively . 1 Verb – noun Idiomatic Combinations Much research on multiword expressions ( MWEs ) in natural language processing ( NLP ) has focused on various type-level prediction tasks , e.g. , MWE extraction ( e.g. , Church and Hanks , 1990 ; Smadja , 1993 ; Lin , 1999 ) — i.e. , determining which MWE types are present in a given corpus ( Baldwin and Kim , 2010 ) — and compositionality prediction ( e.g. , McCarthy et al. , 2003 ; Reddy et al. , 2011 ; Salehi et al. , 2014 ) . However , word combinations can be ambiguous between literal combinations and MWEs .
Neural Inference of API Functions from Input–Output Examples Trials and Tribulations in Synthesizing Operating Systems Deductive Programming Synthesis PRECOMAS an implementation of constructive matching methodology The framework of behavioral program synthesis Meta-Modeling Approach of Building CRM System Using GME-MOF Communication and management support in system development environments THE PREDICATE TRANSFORMER AND ITS APPLICATION IN INTRODUCTION TO PROGRAMMING COURSES The Effectiveness of Global Difference Value Prediction And Memory Bus Priority Schemes for Speculative Prefetch Predicting lab values for gastrointestinal bleeding patients in the intensive care unit: A comparative study on the impact of comorbidities and medications Applying Perceptrons to Speculation in Computer Architecture Generation of a Mean Motion Model of the Lung Using 4D-CT Image Data Value-based branch prediction: an alternative approach to branch prediction Toward an Efficient Prediction of Solar Flares: Which Parameters, and How? A Reinforcement Learning Approach for Host-Based Intrusion Detection Using Sequences of System Calls The Value-Improvement Path: Towards Better Representations for Reinforcement Learning Using fragment chemistry data mining and probabilistic neural networks in screening chemicals for acute toxicity to the fathead minnow An Actor-Critic Algorithm for Sequence Prediction Semantic Independence in DL-Programs Using evolution constraints to assess the failure-proneness of evolving software Common Bug-Fix Patterns: A Large-Scale Observational Study A constraint-based approach to solving games on infinite graphs Some factors affecting program repair maintenance: an empirical study SmartPaste: Learning to Adapt Source Code SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair Automatic Grading and Feedback using Program Repair for Introductory Programming Courses Synthesizing Programs with Constraint Solvers History Driven Program Repair Type inference in flexible model-driven engineering using classification algorithms A L1-regularized feature selection method for local dimension reduction on microarray data Experiments in molecular subtype recognition based on histopathology images Type Prediction in RDF Knowledge Bases Using Hierarchical Multilabel Classification A comparison of the relative costs of financial distress models: artificial neural networks, logit and multivariate discriminant analysis Learning Semantic Annotations for Tabular Data Annotating Character Relationships in Literary Texts Learning from large codebases A Word Embedding Approach to Identifying Verb-Noun Idiomatic Combinations
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2018 2019 1989 1990 2016 2007 1988 2015 2003 2019 2007 2008 2019 2013 2005 2020 2004 2017 2012 1997 2017 2014 1983 2017 2020 2017 2012 2016 2019 2017 2016 2016 1997 2019 2015 2016 2016
PLOS@SOSP ISSAC VCBM ICIC (1) ICLR RR CSMR ESEM POPL ITiCSE CAV SANER ISBI WIMS IJCAI MWE@ACL
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