A logic based approach to reverse engineering tools production

SUMMARY Tools built in an ad hoc way and without proper models often display problems for both tool users and designers. Firstly, without systematic analysis and good understanding of the underlying software process model, we have little chance to design a tool that will adequately address user’s needs. Next, because one tool is often used in many different situations and by people who have different working habits, tools should be flexible and allow a user to customise tools functionalities. Ad hoc built tools usually are not flexible enough, as possible variations in tool functions had not been incorporated into the tool architecture to make future customisations possible. Finally, ad hoc design practice does not lead to accumulating the tool design know-how, makes it difficult to repeat successful solutions and slows down the process of understanding and improving tool design methods. We applied conceptual modelling in design of tools for software maintenance to alleviate some of the above problems. In this paper, we describe a model-based method to designing reverse engineering tools. The design starts by modelling low level source program design models, higher level design models to be recovered and heuristic rules a reverse engineering tool uses to recover higher level designs from lower level designs. On one hand, conceptual models lead to better understanding of tool requirements. On the other hand, model-based approach leads to the design of a generic design abstractor, a component of a reverse engineering tool that evaluates reverse engineering heuristics. A generic design abstractor adds flexibility to reverse engineering tools in two ways: (1) we can customise the generic design abstractor to meet requirements of a reverse engineering project in hand and (2) a programmer (an end-user of a reverse engineering tool) can define new reverse engineering heuristics and tune in recovered designs.

Despite advances in natural language processing (NLP), extracting information from clinical text is expensive. Interactive tools that are capable of easing the construction, review, and revision of NLP models can reduce this cost and improve the utility of clinical reports for clinical and secondary use. We present the design and implementation of an interactive NLP tool for identifying incidental findings in radiology reports, along with a user study evaluating the performance and usability of the tool. Expert reviewers provided gold standard annotations for 130 patient encounters (694 reports) at sentence, section, and report levels. We performed a user study with 15 physicians to evaluate the accuracy and usability of our tool. Participants reviewed encounters split into intervention (with predictions) and control conditions (no predictions). We measured changes in model performance, the time spent, and the number of user actions needed. The System Usability Scale (SUS) and an open-ended questionnaire were used to assess usability. Starting from bootstrapped models trained on 6 patient encounters, we observed an average increase in F1 score from 0.31 to 0.75 for reports, from 0.32 to 0.68 for sections, and from 0.22 to 0.60 for sentences on a held-out test data set, over an hour-long study session. We found that tool helped significantly reduce the time spent in reviewing encounters (134.30 vs. 148.44 seconds in intervention and control, respectively), while maintaining overall quality of labels as measured against the gold standard. The tool was well received by the study participants with a very good overall SUS score of 78.67. The user study demonstrated successful use of the tool by physicians for identifying incidental findings. These results support the viability of adopting interactive NLP tools in clinical care settings for a wider range of clinical applications.

Semantic Web systems provide open interfaces for end-users to access data via a powerful high-level query language, SPARQL. But users unfamiliar with either the details of SPARQL or properties of the target dataset may find it easier to query by example -- give examples of the information they want (or examples of both what they want and what they do not want) and let the system reverse engineer the desired query from the examples. This approach has been heavily used in the setting of relational databases. We provide here an investigation of the reverse engineering problem in the context of SPARQL. We first provide a theoretical study, formalising variants of the reverse engineering problem and giving tight bounds on its complexity. We next explain an implementation of a reverse engineering tool for positive examples. An experimental analysis of the tool shows that it scales well in the data size, number of examples, and in the size of the smallest query that fits the data. We also give evidence that reverse engineering tools can provide benefits on real-life datasets.

Reverse engineering tools support software engineers in the process of analysing and understanding complex software systems during maintenance, re-engineering or re-architecturing. The functionality of such tools varies from editing and browsing capabilities to the generation of textual and graphical reports. There are several commercial reverse engineering tools on the market providing different capabilities and supporting specific source code languages. We evaluated four reverse engineering tools that analyse C source code: Refine/C, Imagix 4D, SNiFF+ and Rigi. We investigated the capabilities of these tools by applying them to a real-world embedded software system as a case study. We identified benefits and shortcomings of these tools and assessed their applicability for embedded software systems, their usability and their extensibility. © 1998 John Wiley & Sons, Ltd.

Developers tend to leave some important steps and actions (e.g. properly designing the system's architecture, code review and testing) out of the software development process, and use risky practices (e.g. the copy-paste technique) so that the software can be released as fast as possible. However, these practices may turn out to be critical from the viewpoint of maintainability of the software system. In such cases, a cost-effective solution might be to re-engineer the system. Re-engineering consists of two stages, namely reverse-engineering information from the current system and, based on this information, forward-engineering the system to a new form. In this way, successful re-engineering significantly depends on the reverse engineering phase. Therefore, it is vital to guarantee correctness, and to improve the results of the reverse engineering step. Otherwise, the re-engineering of the software system could fail due to the bad results of reverse engineering. The above issues motivated us to develop a method which extends and improves one of our reverse engineering tools, and to develop benchmarks and to perform experiments on evaluating and comparing reverse engineering tools.

To support program comprehension, maintenance, and evolution, metamodels are frequently used during program reverse engineering activities to describe and analyze constituents of a program and their relations. Reverse engineering tools often define their own metamodels according to the intended purposes and features. Although each metamodel has its own advantages, its limitations may be addressed by other metamodels. Existing works have evaluated and compared metamodels and tools, but none have considered all the possible characteristics and limitations to provide a comprehensive guideline for classifying, comparing, reusing, and extending program metamodels. To aid practitioners and researchers in classifying, comparing, reusing, and extending program metamodels and their corresponding reverse engineering tools according to the intended goals, we establish a conceptual framework with definitions of program metamodels and related concepts. Then this framework is used to provide a comprehensive taxonomy, named Program Metamodel TAxonomy (ProMeTA), which incorporates newly identified characteristics into those stated in previous works, which were identified via a systematic literature survey on program metamodels, while keeping the orthogonality of the entire taxonomy. Additionally, we validate the taxonomy in terms of its orthogonality and usefulness through the classification of popular metamodels.

all the unnecessary information of data structures and types is removed at the end of compilation. However, the high level information is extremely valuable and necessary for many fields such as debugging, reverse engineering, security etc. Without data structures information, reverse engineering is extremely hard to perform. Unfortunately, even the most advanced techniques and tools for reverse engineering are very weak in this part. Recently, several new solutions have been proposed and each has made some achievements. In this paper, we summarize challenges in this problem, then analyze three existing outstanding approaches, discuss their limitations and at last propose some possible directions to make further efforts.

In this study, the aim is to create and improve a methodology for synthesizing requirements and specifications for the re-engineering of IT projects with maximum efficiency and business orientation. The main task is to adapt outdated IT systems to the changing technical environment, in particular to cloud technologies and security system requirements. To achieve these goals, the proposed methodology uses the analysis of archaic systems, the reverse engineering method, expert surveys, documentation analysis, and black-box modeling. The application of these methods allows for the identification and revision of requirements and specifications, ensuring a high level of quality and efficiency in the process of re-engineering IT projects. The article further discusses the practical aspects of applying the methodology, prospects for further development, and the peculiarities of using various statistical methods in the process of improving re-engineering results. The operating principles of the method are described along with the main approaches and techniques that promote the analysis of existing IT systems, the synthesis of requirements and specifications, quality control, and successful project implementation. The individual components of the method include the collection of data about the existing system and the analysis of archaic systems to restore the definition of requirements. The use of the black-box model for testing the developed system is discussed, including the analysis of the obtained results, correction of requirements, and improvement of specifications. The methodology includes documentation analysis tools, reverse engineering, surveys and data visualization tools, as well as analytical techniques such as a formula for parallel testing, a formula for requirement traceability matrix, and a formula for forecasting requirements based on discrepancy rate analysis. As a result of implementing the IT project reengineering method, successful transition from old to new technologies can be achieved, the IT industry can be optimized, and conditions can be created for adaptation to modern technical environments, ensuring stability and reliability of the implemented reengineering projects. Based on the analysis of modern sources, previous experience, and conducted research, it can be asserted that the method for synthesizing specifications and requirements in the process of reengineering IT projects is of great importance and relevance for the modern development of information technology and business processes.

This study examines the expression of students’ motivation and engagement in primary mathematics education through the use of interactive digital tools. The relevance of the topic is highlighted by the increasing integration of digital technologies into the educational process, aiming to enhance students’ active participation, deepen understanding of mathematical concepts, and develop essential competencies that form the foundation for further learning. Mathematics in primary education plays a crucial role in developing conceptual understanding, procedural fluency, strategic problem-solving skills, logical and spatial reasoning, the ability to communicate mathematical ideas, and collaboration with peers. Additionally, fostering a positive attitude toward mathematics is particularly important, allowing students to perceive the subject's relevance and applicability in everyday life. Digital tools such as GeoGebra, Matific, ClassWise, augmented reality (AR), virtual reality (VR), and the EDUKA platform support these objectives by promoting student motivation, engagement, and academic achievement (Bertrand et al., 2024; Radović et al., 2018; Gulbinas & Arkušauskaitė, 2015). The research problem is based on the observation that although interactive digital tools are increasingly used in mathematics lessons, empirical data on their impact on student motivation and engagement are limited. Student behavior, activity levels, and collaboration depend on lesson structure, pedagogical methods, classroom microclimate, and the nature of the tools used, necessitating analysis in authentic educational contexts (Mula et al., 2025; Radišić & Baucal, 2024). The study aimed to analyze students’ motivation and engagement during mathematics lessons employing interactive digital tools. Research tasks included reviewing scientific literature, outlining the advantages and challenges of integrating digital tools, and analyzing observational data to identify forms of behavioral and emotional motivation. The study employed a qualitative research methodology, using structured observation to systematically record students’ engagement and motivation in lessons incorporating GeoGebra, Matific, ClassWise, AR/VR, and EDUKA. Data were analyzed using qualitative content analysis to identify key themes and interpret findings (Kardelis, 2016). The observation protocol focused on three domains: student behavior, emotional expression, and teacher activity. Approximately 40 students and four teachers from two Lithuanian primary schools participated in eight mathematics lessons, allowing for a diverse dataset reflecting different educational contexts. Results revealed that student engagement and motivation are closely linked to the type of interactive tool, the incorporation of gamified elements, and the structure of lesson activities. Lessons using Matific, GeoGebra, or ClassWise promoted higher attention, active participation, collaboration, and emotional involvement. Gamification features, such as point collection and visual performance indicators, enhanced extrinsic motivation, while Matific’s star ratings supported intrinsic motivation, encouraging students to achieve understanding beyond task completion (Noverianto & Munahefi, 2023). Less interactive tools, such as EDUKA modules without gamified features, elicited lower engagement and mechanical task completion. AR and VR applications stimulated curiosity, spatial reasoning, and visual understanding, although technical difficulties occasionally reduced motivation temporarily. Nevertheless, students’ overall responses to AR/VR experiences were positive, demonstrating interest and willingness to repeat activities (Bulut & Borromeo Ferri, 2023; Cao, 2023). The findings also indicated that long-term motivation depends on pedagogical context—consistent teacher support, structured lessons, clear objectives, and high interactivity levels reinforce not only short-term engagement but also sustained interest in mathematics. The study highlighted that technical challenges, including equipment availability, system failures, and platform licensing, can temporarily hinder motivation. Pedagogical preparedness is critical, as even advanced technologies do not yield results without structured lesson planning, gamification, and teacher guidance. Digital tools are most effective when combined with a positive classroom climate, social context, and diverse pedagogical strategies. In conclusion, interactive digital tools significantly enhance students’ motivation, engagement, collaboration, and ability to visualize complex mathematical phenomena. Their effectiveness, however, depends on teacher preparation, technological competence, classroom microclimate, and proper structuring of activities. Integrating interactive, gamified digital resources with social and pedagogical support provides conditions for meaningful, effective, and sustained learning in primary mathematics. The study emphasizes that successful digital tool implementation can maintain intrinsic motivation, foster ongoing learning enthusiasm, and improve academic outcomes, while also providing teachers with flexibility to differentiate tasks according to individual student abilities.

Abstract. In this study, a customized hinged ankle-foot orthosis (AFO) was de-signed by using reverse engineering and computer-aided design (CAD) tools.. For that, the following three steps were followed: first, obtain a digital replica of the foot, ankle and lower leg area to be covered by the AFO by using a 3D scan-ner; second, create the internal and external surfaces of the AFO in a reverse en-gineering software program; and third, create the design of the AFO in a CAD software program. These types of methods for creating AFOs are less labor in-tensive than traditional ones, and enable to create AFOs of different materials, in less time and with better fit and aesthetics. Keywords: Ankle-foot orthosis, customization, computer-aided design, reverse engineering, 3D scanner.

This paper focuses on some issues relating to data modelling, quality and management in a specific domain: forests. Many forest domain specialists e.g., botanists, zoologists, economists and others collect vast volumes of data about the forest fauna and flora, climate, soil, etc. The favourite tools for managing this data are spreadsheets and/or using popular DBMS packages such as Access or FoxPro. The use of these tools introduces two major problems: loss of semantics and poor data structure. These problems and associated issues are examined in this the paper.To address these problems, we propose a method for database reverse engineering from spreadsheet tables to a conceptual model and suggest a design of a prototype tool (TREAT). We also explain the motivation for and the methodology and approach that we adopted. The interactive process used to identify the constituents of the spreadsheet tables and data semantics are explained. Semi-automated analysis of the associations between the data items in terms of the domain knowledge, constraints and functional dependencies between the data items are also described. The output from the tool may be selected as either an Entity-Relationship or Object or Object-Relational model.

Before performing software maintenance and evolution tasks, developers must expend significant effort to understand the design of the subject software system. Reverse engineering and reengineering tools can extract artifacts, such as class diagrams, from source code so that the effort expended on program comprehension can be greatly reduced. Choosing suitable and appropriate tools is itself a difficult process. Many metrics and benchmarks have been proposed to evaluate the quality of reengineering tools, but the process is still complicated and requires much human effort. Furthermore, ignored by most evaluation experiments and approaches is the understanding of differences and similarities of the output artifacts produced by different tools. This paper proposes a novel approach for tool evaluation making a direct comparison of the output artifacts of the chosen tools. We apply our methodology on class diagrams extracted from source code. We utilize a class matching model to automate the process. In this study, we use ten open-source programs as test cases and two open source reverse engineering tools Doxygen and StarUML. We evaluate the output of these two tools based on class matching given the same input. This approach, we believe, provides developers with good guidance in tool selection.

Cybersecurity attacks on embedded devices for industrial control systems and cyber-physical systems may cause catastrophic physical damage as well as economic loss. This could be achieved by infecting device binaries with malware that modifies the physical characteristics of the system operation. Mitigating such attacks benefits from reverse engineering tools that recover sufficient semantic knowledge in terms of mathematical equations of the implemented algorithm. Conventional reverse engineering tools can decompile binaries to low-level code, but offer little semantic insight. This article proposes the REMaQE automated framework for reverse engineering of math equations from binary executables. Improving over state-of-the-art, REMaQE handles equation parameters accessed via registers, the stack, global memory, or pointers, and can reverse engineer equations from object-oriented implementations such as C++ classes. Using REMaQE, we discovered a bug in the Linux kernel thermal monitoring tool “tmon.” To evaluate REMaQE, we generate a dataset of 25,096 binaries with math equations implemented in C and Simulink. REMaQE successfully recovers a semantically matching equation for all 25,096 binaries. REMaQE executes in 0.48 seconds on average and in up to 2 seconds for complex equations. Real-time execution enables integration in an interactive math-oriented reverse engineering workflow.

Knowing which associations are compositions is important in a tool for the reverse engineering of UML class diagrams. Firstly, recovery of composition relationships bridges the gap between design and code. Secondly, since composition relationships explicitly state a requirement that certain representations cannot be exposed, it is important to determine if this requirement is met by component code. Verifying that compositions are implemented properly may prevent serious program flaws due to representation exposure. We propose an implementation-level composition model based on ownership and a novel approach for identifying compositions in Java software. Our approach is based on static ownership inference; it is parameterized by class analysis and is designed to work on incomplete programs. We present empirical results from two instantiation of our approach. For one of these instantiations, on average 40% of the examined fields account for relationships that are identified as compositions. In addition, we present a precision evaluation which shows that the analysis achieves almost perfect precision--that is, it almost never misses composition relationships. The results indicate that precise identification of interclass relationships can be done with a simple and inexpensive analysis, and thus can be easily incorporated in reverse engineering tools that support iterative model-driven development.

Damage of runners and other components of hydro turbine by sediment erosion and cavitation erosion is an inevitable problem associated with hydropower established in a region with mountainous topology. The paper attempts to investigate the suitability and sustainability of the reverse engineering method for the design of the eroded Francis runner. This manufacturing technology is in a progressive phase of development in various parts of the world, so research scholars have been constantly working on the concept to increase the speed and efficiency of the method. The case investigation was conducted on Puwa-Khola 1HPP eroded Francis runner. The paper investigates reverse engineering design methodology of the eroded runner and discusses scope and limitation in the utilization of method at Hydropower sector. Also, the paper suggests suitable 3D scanning techniques, reverse engineering tools, and optimization methods for obtaining mathematically sound models without any hydraulic profile deviation between actual and reversed engineered model.