Bidirectional live programming systems (BLP) enable developers to modify a program by directly manipulating the program output, so that the updated program can produce the manipulated output. One state-of-the-art approach to BLP systems is operation-based, which captures the developer's intention of program modifications by taking how the developer manipulates the output into account. The program modifications are usually hard coded for each direct manipulation in these BLP systems, which are difficult to extend. Moreover, to reflect the manipulations to the source program, these BLP systems trace the modified output to appropriate code fragments and perform corresponding code transformations. Accordingly, they require direct manipulation users be aware of the source code and how it is changed, making “direct” manipulation (on output) be “indirect”. In this paper, we resolve this problem by presenting a novel operation-based framework for bidirectional live programming, which can automatically fuse direct manipulations into the source code, thus supporting code-insensitive direct manipulations. Firstly, we design a simple but expressive delta language DM capable of expressing common direct manipulations for output values. Secondly, we present a fusion algorithm that propagates direct manipulations into the source functional programs and applies them to the constants whenever possible; otherwise, the algorithm embeds manipulations into the “proper positions” of programs. We prove the correctness of the fusion algorithm that the updated program executes to get the manipulated output. To demonstrate the expressiveness of DM and the effectiveness of our fusion algorithm, we have implemented FuseDM, a prototype SVG editor that supports GUI-based operations for direct manipulation, and successfully designed 14 benchmark examples starting from blank code using FuseDM.

In order to overcome the problems of low data reliability and long generation time of traditional automatic generation methods of software test data, an automatic generation method of software test data based on improved Markov model is designed. Firstly, collect software test data in different stages; Then, by calculating the similarity of the collected software test data, remove the test data with high similarity, calculate the importance of the software test data with the help of entropy weight method, and complete the data preprocessing; Finally, the Markov model is improved with the help of genetic algorithm, generation path and variation factor of software test data are set, and the improved Markov model is used to automatically generate high quality software test data. Experimental results show that when the number of experiments is 50, the generation time of this method is about 2.8 s, the reliability coefficient is always higher than 0.8.

Electronic medical records (EMRs) are scattered in different hospitals, which hinders the process of data sharing. On the other hand, people with different roles should access different parts of data, thus we need a way to control the accessibility. To address these issues, we propose a blockchain-based data sharing system that gathers EMRs into blockchain and controls data sharing through bidirectional transformation. In our system, EMRs are encoded with a carefully designed data structure that stores not only data, but also data’s read/write permission for different roles. We redesigned a bidirectional transformation language based on our previous work BiGUL by taking access control into consideration, and the accessibility are checked during program execution in both direction to avoid un-authorized data access. Further more, each bidirectional transformation program and updates on the shared data are stored in the blockchain in a transaction-like form. The immutability of blockchain guarantees EMR’s data integrity.

Goal-oriented adaptation provides a powerful mechanism to develop self-adaptive systems, enabling systems to keep satisfying user goals in a dynamically changing environment. The goal-oriented approach normally reduces the adaptation planning as a global optimization process and leaves the system the task of determining the actions required to achieve the goals. However, the high computation cost of global optimization prevents a self-adaptive system from quickly adjusting itself to the dynamically changing environment at runtime, which is intolerable since efficiency of planning is of utmost importance in most self-adaptive systems. On the other hand, rule-based adaptation has the advantage of efficient planning process since it predefines the adaptation logic by rules instead of leaving the system the task of reasoning. To combine the advantages of both approaches, we propose a novel adaptation framework that can integrate goal model into rule-based adaptation to make user goals to be better satisfied efficiently. We have applied the framework to design a self-adaptive e-commerce website. Our experimental results show that the proposed framework outperforms both the traditional goal-oriented approach and the traditional rule-based approach in terms of adaptation efficiency and effectiveness.

Putback-based bidirectional programming allows the programmer to write only one putback transformation, from which the unique corresponding forward transformation is derived for free. The logic of a putback transformation is more sophisticated than that of a forward transformation and does not always give rise to well-behaved bidirectional programs; this calls for more robust language design to support development of well-behaved putback transformations. In this paper, we design and implement a concise core language BiGUL for putback-based bidirectional programming to serve as a foundation for higher-level putback-based languages. BiGUL is completely formally verified in the dependently typed programming language Agda to guarantee that any putback transformation written in BiGUL is well-behaved.

Due to various considerations, programmers often need to backtrack their code. Furthermore, as the most recent edit may not be the wrong edit, programmers sometimes have to backtrack their code for arbitrary edits, which is referred as selective undo in this paper. To meet the needs, researchers have proposed various approaches to support selective undo. However, to the best of our knowledge, these approaches can support only simple edits, and cannot handle refactoring, although most code editors already provide various refactoring actions. Indeed, it is challenging to support selective undo for refactoring, since multiple code elements and complicated actions can be involved. In this paper, we present a novel approach that leverages Bidirectional Transformation (BX) to support selective undo for refactoring. We evaluate our approach on a recent refactoring tool that transfers enhanced for loops to lambda expressions. Our results show that our approach achieves an accuracy of up to 89%.

Different XML formats are widely used for data exchange and processing, being often necessary to mutually convert between them. Standard XML transformation languages, like XSLT or XQuery, are unsatisfactory for this purpose since they require writing a separate transformation for each direction. Existing bidirectional transformation languages mean to cover this gap, by allowing programmers to write a single program that denotes both transformations. However, they often 1) induce a more cumbersome programming style than their traditionally unidirectional relatives, to establish the link between source and target formats, and 2) offer limited configurability, by making implicit assumptions about how modifications to both formats should be translated that may not be easy to predict.