Type Inference Method Research Articles

Combining type inference techniques for semi-automatic UML generation from Pharo code

This paper explores how to reconstruct UML diagrams from dynamically typed languages such as Smalltalk, which do not use explicit type information. This lack of information makes traditional methods for extracting associations difficult. It addresses the need for automated techniques, particularly in legacy software systems, to facilitate their transformation into modern technologies, focusing on Smalltalk as a case study due to its extensive industrial legacy and modern adaptations like Pharo. We propose a way to create UML diagrams from Smalltalk code, focusing on using type inference to determine UML associations. For optimal outcomes for large-scale software systems, we recommend combining different type inference methods in an automatic or semi-automatic way.

ScLEGA: an attention-based deep clustering method with a tendency for low expression of genes on single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) enables the exploration of biological heterogeneity among different cell types within tissues at a resolution. Inferring cell types within tissues is foundational for downstream research. Most existing methods for cell type inference based on scRNA-seq data primarily utilize highly variable genes (HVGs) with higher expression levels as clustering features, overlooking the contribution of HVGs with lower expression levels. To address this, we have designed a novel cell type inference method for scRNA-seq data, termed scLEGA. scLEGA employs a novel zero-inflated negative binomial (ZINB) loss function that fully considers the contribution of genes with lower expression levels and combines two distinct scRNA-seq clustering strategies through a multi-head attention mechanism. It utilizes a low-expression optimized denoising autoencoder, based on the novel ZINB model, to extract low-dimensional features and handle dropout events, and a GCN-based graph autoencoder (GAE) that leverages neighbor information to guide dimensionality reduction. The iterative fusion of denoising and topological embedding in scLEGA facilitates the acquisition of cluster-friendly cell representations in the hidden embedding, where similar cells are brought closer together. Compared to 12 state-of-the-art cell type inference methods on 15 scRNA-seq datasets, scLEGA demonstrates superior performance in clustering accuracy, scalability, and stability. Our scLEGA model codes are freely available at https://github.com/Masonze/scLEGA-main.

FQN Inference in Partial Code by Prompt-tuned Language Model of Code

Partial code usually involves non-fully-qualified type names (non-FQNs) and undeclared receiving objects. Resolving the FQNs of these non-FQN types and undeclared receiving objects (referred to as type inference) is the prerequisite to effective search and reuse of partial code. Existing dictionary-lookup based methods build a symbolic knowledge base of API names and code contexts, which involve significant compilation overhead and are sensitive to unseen API names and code context variations. In this article, we propose using a p rompt-tuned c o de m asked language mod e l (MLM) as a neural knowledge base for type inference, called POME, which is lightweight and has minimal requirements on code compilation. Unlike the existing symbol name and context matching for type inference, POME infers the FQNs syntax and usage knowledge encapsulated in prompt-tuned code MLM through a colze-style fill-in-blank strategy. POME is integrated as a plug-in into web and integrated development environments (IDE) to assist developers in inferring FQNs in the real world. We systematically evaluate POME on a large amount of source code from GitHub and Stack Overflow, and explore its generalization and hybrid capability. The results validate the effectiveness of the POME design and its applicability for partial code type inference, and they can be easily extended to different programming languages (PL). POME can also be used to generate a PL-hybrid type inference model for providing a one-for-all solution. As the first of its kind, our neural type inference method opens the door to many innovative ways of using partial code.

Ptype: probabilistic type inference

Type inference refers to the task of inferring the data type of a given column of data. Current approaches often fail when data contains missing data and anomalies, which are found commonly in real-world data sets. In this paper, we propose ptype, a probabilistic robust type inference method that allows us to detect such entries, and infer data types. We further show that the proposed method outperforms existing methods.

Fuzzy Expert System for Prediction of Prostate Cancer

A fuzzy expert system (FES) for the prediction of prostate cancer (PC) is prescribed in this paper. Age, prostate-specific antigen (PSA), prostate volume (PV) and [Formula: see text] Free PSA ([Formula: see text]FPSA) are fed as inputs into the FES and prostate cancer risk (PCR) is obtained as the output. Using knowledge-based rules in Mamdani type inference method the output is calculated. If PCR [Formula: see text], then the patient shall be advised to go for a biopsy test for confirmation. The efficacy of the designed FES is tested against a clinical dataset. The true prediction for all the patients turns out to be [Formula: see text] whereas only for positive biopsy cases it rises to [Formula: see text]. This simple yet effective FES can be used as supportive tool for decision-making in medical diagnosis.

A data type inference method based on long short-term memory by improved feature for weakness analysis in binary code

Abstract As software is used in various areas today, software security has become a crucial issue. Third-party libraries, which play a major role in software development, pose difficulties in analyzing and testing software security. It is essential to know the variables used in software and the data type information of each variable in order to identify the major weaknesses in the software. However, because the third-party library is generally of the binary code form, the variables, variable data type, program syntax, and semantic information in the source code are removed. Therefore, reconstructing the variables used and the data type information of the variables from binary code is the most important step in weak point analysis. Traditionally, this step of reconstructing information is based on pattern matching; however, the inference of data types is limited. We herein proposed a method of inferring data types using deep learning for variables determined based on pattern matching in binary code, and analyzed its performance. The proposed study has improved the feature generation method to solve the inconsistent problems of the features generated in the previous studies. As a result, the accuracy of prediction of float and double is improved by average 7.2% compared to the previous study, and the result is that the accuracy of 5.1% is increased overall.

An Approach to Composition Based MISO Algorithms for Fuzzy Inference Schemes Based on the View of Weighted Information

In this article, we will construct multiple-input, single-output (MISO) algorithms for fuzzy inference schemes based on the techniques of aggregation of weighted information. First, by an example, we examine various aspects of a crisp discrete multivariable function under fuzzy environment and obtain many its new functional representations. Next, following the merits of such representations, we build four classes of (new) composition based MISO algorithms as a realization of fuzzy interpolation (extension) of the previous new functional representations, in which, the class of Mamdani-type MISO algorithms are rediscovered. Finally, as an application, we provide an alternative approach to the equivalence conditions of some fuzzy inference methods issued by Seki and Mizumoto recently. In Seki and Mizumoto's approach, they showed that three widely used as fuzzy control methods— the product-sum-gravity method, the simplified fuzzy inference method and the fuzzy singleton- type inference method, are equivalent to each other; and obtained some equivalence conditions between these three methods with two other fuzzy inference methods, while in our approach, we show that all these fuzzy inference methods are closely related with a MISO algorithm of specific type.

Automating relatively complete verification of higher-order functional programs

We present an automated approach to relatively completely verifying safety (i.e., reachability) property of higher-order functional programs. Our contribution is two-fold. First, we extend the refinement type system framework employed in the recent work on (incomplete) automated higher-order verification by drawing on the classical work on relatively complete "Hoare logic like" program logic for higher-order procedural languages. Then, by adopting the recently proposed techniques for solving constraints over quantified first-order logic formulas, we develop an automated type inference method for the type system, thereby realizing an automated relatively complete verification of higher-order programs.

Content-Based Motion Estimation with Extended Temporal-Spatial Analysis

In adaptive motion estimation, spatial-temporal correlation based motion type inference has been recognized as an effective way to guide the motion estimation strategy adjustment according to video contents. However, the complexity and the reliability of those methods remain two crucial problems. In this paper, a motion vector field model is introduced as the basis for a new spatial-temporal correlation based motion type inference method. For each block, Full Search with Adaptive Search Window (ASW) and Three Step Search (TSS), as two search strategy candidates, can be employed alternatively. Simulation results show that the proposed method can constantly reduce the dynamic computational cost to as low as 3% to 4% of that of Full Search (FS), while remaining a closer approximation to FS in terms of visual quality than other fast algorithms for various video sequences. Due to its efficiency and reliability, this method is expected to be a favorable contribution to the mobile video communication where low power real-time video coding is necessary.

Local type inference

We study two partial type inference methods for a language combining subtyping and impredicative polymorphism. Both methods are local in the sense that missing annotations are recovered using only information from adjacent nodes in the syntax tree, without long-distance constraints such as unification variables. One method infers type arguments in polymorphic applications using a local constraint solver. The other infers annotations on bound variables in function abstractions by propagating type constraints downward from enclosing application nodes. We motivate our design choices by a statistical analysis of the uses of type inference in a sizable body of existing ML code.