Automated Software Reengineering Model and Framework

Тодор П. Чолаков, Димитър Й. Биров - Тази статия представя цялостен модел за автоматизиран реинженеринг на наследени системи. Тя описва в детайли процесите на превод на софтуера и на рефакторинг и степента, до която могат да се автоматизират тези процеси. По отношение на превода на код се представя модел за автоматизирано превеждане на код, съдържащ указатели и работа с адресна аритметика. Също така се дефинира рамка за процеса на реинженеринг и се набелязват възможности за по-нататъшно развитие на концепции, инструменти и алгоритми.

ASDN : Automated Software Design Notebook tool

Software Engineering is one of the most widely researched areas of Computer Science. The ability to reuse software, much like reuse of hardware components is one of the key issues in software development. The object-oriented programming methodology is revolutionary in that it promotes software reusability. This thesis describes the development of a tool that helps programmers to design and implement software from within the Smalltalk Environment (an Object- Oriented programming environment). The ASDN tool is part of the PEREAM (Programming Environment for the Reuse and Evolution of Abstract Models) system, which advocates incremental development of software. The Asdn tool along with the PEREAM system seeks to enhance the Smalltalk programming environment by providing facilities for structured development of abstractions (concepts). It produces a document that describes the abstractions that are developed using this tool. The features of the ASDN tool are illustrated by an example.

basic research in computer science and software engineering at sklcs

The State Key Laboratory of Computer Science (SKLCS) is committed to basic research in computer science and software engineering. The research topics of the laboratory include: concurrency theory, theory and algorithms for real-time systems, formal specifications based on context-free grammars, semantics of programming languages, model checking, automated reasoning, logic programming, software testing, software process improvement, middleware technology, parallel algorithms and parallel software, computer graphics and human-computer interaction. This paper describes these topics in some detail and summarizes some results obtained in recent years.

Uma abordagem sistemática para implementação, gerenciamento e customização de testes de linhas de produto de software

Through the adoption of the software product line (SPL) approach, several benefits are achieved when compared to the conventional development processes that are based on creating a single software system at a time. The process of developing a SPL differs from traditional software construction, since it has two essential phases: the domain engineering - when common and variables elements of the SPL are defined and implemented; and the application engineering - when one or more applications (specific products) are derived from the reuse of artifacts created in the domain engineering. The test activity is also fundamental and aims to detect defects in the artifacts produced in SPL development. However, the characteristics of an SPL bring new challenges to this activity that must be considered. Several approaches have been recently proposed for the testing process of product lines, but they have been shown limited and have only provided general guidelines. In addition, there is also a lack of tools to support the variability management and customization of automated case tests for SPLs. In this context, this dissertation has the goal of proposing a systematic approach to software product line testing. The approach offers: (i) automated SPL test strategies to be applied in the domain and application engineering, (ii) explicit guidelines to support the implementation and reuse of automated test cases at the unit, integration and system levels in domain and application engineering; and (iii) tooling support for automating the variability management and customization of test cases. The approach is evaluated through its application in a software product line for web systems. The results of this work have shown that the proposed approach can help the developers to deal with the challenges imposed by the characteristics of SPLs during the testing process

COLLABORATIVE TESTING ACROSS SHARED SOFTWARE COMPONENTS

Large component-based systems are often built from many of the same components. As individual component-based software systems are developed, tested and maintained, these shared components are repeatedly manipulated. As a result there are often significant overlaps and synergies across and among the different test efforts of different component-based systems. However, in practice, testers of different systems rarely collaborate, taking a test-all-by-yourself approach. As a result, redundant effort is spent testing common components, and important information that could be used to improve testing quality is lost. The goal of this research is to demonstrate that, if done properly, testers of shared software components can save effort by avoiding redundant work, and can improve the test effectiveness for each component as well as for each component-based software system by using information obtained when testing across multiple components. To achieve this goal I have developed collaborative testing techniques and tools for developers and testers of component-based systems with shared components, applied the techniques to subject systems, and evaluated the cost and effectiveness of applying the techniques. The dissertation research is organized in three parts. First, I investigated current testing practices for component-based software systems to find the testing overlap and synergy we conjectured exists. Second, I designed and implemented infrastructure and related tools to facilitate communication and data sharing between testers. Third, I designed two testing processes to implement different collaborative testing algorithms and applied them to large actively developed software systems. This dissertation has shown the benefits of collaborative testing across component developers who share their components. With collaborative testing, researchers can design algorithms and tools to support collaboration processes, achieve better efficiency in testing configurations, and discover inter-component compatibility faults within a minimal time window after they are introduced.

A Binary Classifier for Test Case Feasibility Applied to Automatically Generated Tests of Event-Driven Software

Modern software application testing, such as the testing of software driven by graphical user interfaces (GUIs) or leveraging event-driven architectures in general, requires paying careful attention to context. Model-based testing (MBT) approaches first acquire a model of an application, then use the model to construct test cases covering relevant contexts. A major shortcoming of state-of-the-art automated model-based testing is that many test cases proposed by the model are not actually executable. These \textit{infeasible} test cases threaten the integrity of the entire model-based suite, and any coverage of contexts the suite aims to provide. In this research, I develop and evaluate a novel approach for classifying the feasibility of test cases. I identify a set of pertinent features for the classifier, and develop novel methods for extracting these features from the outputs of MBT tools. I use a supervised logistic regression approach to obtain a model of test case feasibility from a randomly selected training suite of test cases. I evaluate this approach with a set of experiments. The outcomes of this investigation are as follows: I confirm that infeasibility is prevalent in MBT, even for test suites designed to cover a relatively small number of unique contexts. I confirm that the frequency of infeasibility varies widely across applications. I develop and train a binary classifier for feasibility with average overall error, false positive, and false negative rates under 5\%. I find that unique event IDs are key features of the feasibility classifier, while model-specific event types are not. I construct three types of features from the event IDs associated with test cases, and evaluate the relative effectiveness of each within the classifier. To support this study, I also develop a number of tools and infrastructure components for scalable execution of automated jobs, which use state-of-the-art container and continuous integration technologies to enable parallel test execution and the persistence of all experimental artifacts.

Machine learning techniques to improve software quality

A significant proportion of the cost of software development is due to software testing and maintenance. This is in part the result of the inevitable imperfections due to human error, lack of quality during the design and coding of software, and the increasing need to reduce faults to improve customer satisfaction in a competitive marketplace. Given the cost and importance of removing errors improvements in fault detection and removal can be of significant benefit. The earlier in the development process faults can be found, the less it costs to correct them and the less likely other faults are to develop. This research aims to make the testing process more efficient and effective by identifying those software modules most likely to contain faults, allowing testing efforts to be carefully targeted. This is done with the use of machine learning algorithms which use examples of fault prone and not fault prone modules to develop predictive models of quality. In order to learn the numerical mapping between module and classification, a module is represented in terms of software metrics. A difficulty in this sort of problem is sourcing software engineering data of adequate quality. In this work, data is obtained from two sources, the NASA Metrics Data Program, and the open source Eclipse project. Feature selection before learning is applied, and in this area a number of different feature selection methods are applied to find which work best. Two machine learning algorithms are applied to the data - Naive Bayes and the Support Vector Machine - and predictive results are compared to those of previous efforts and found to be superior on selected data sets and comparable on others. In addition, a new classification method is proposed, Rank Sum, in which a ranking abstraction is laid over bin densities for each class, and a classification is determined based on the sum of ranks over features. A novel extension of this method is also described based on an observed polarising of points by class when rank sum is applied to training data to convert it into 2D rank sum space. SVM is applied to this transformed data to produce models the parameters of which can be set according to trade-off curves to obtain a particular performance trade-off.

Iterative development of MobileMums : a physical activity intervention for women with young children.

Background To describe the iterative development process and final version of ‘MobileMums’: a physical activity intervention for women with young children (<5 years) delivered primarily via mobile telephone (mHealth) short messaging service (SMS). Methods MobileMums development followed the five steps outlined in the mHealth development and evaluation framework: 1) conceptualization (critique of literature and theory); 2) formative research (focus groups, n= 48); 3) pre-testing (qualitative pilot of intervention components, n= 12); 4) pilot testing (pilot RCT, n= 88); and, 5) qualitative evaluation of the refined intervention (n= 6). Results Key findings identified throughout the development process that shaped the MobileMums program were the need for: behaviour change techniques to be grounded in Social Cognitive Theory; tailored SMS content; two-way SMS interaction; rapport between SMS sender and recipient; an automated software platform to generate and send SMS; and, flexibility in location of a face-to-face delivered component. Conclusions The final version of MobileMums is flexible and adaptive to individual participant’s physical activity goals, expectations and environment. MobileMums is being evaluated in a community-based randomised controlled efficacy trial (ACTRN12611000481976).

A taxonomy of model-based testing approaches

Model-based testing (MBT) relies on models of a system under test and/or its environment to derive test cases for the system. This paper discusses the process of MBT and defines a taxonomy that covers the key aspects of MBT approaches. It is intended to help with understanding the characteristics, similarities and differences of those approaches, and with classifying the approach used in a particular MBT tool. To illustrate the taxonomy, a description of how three different examples of MBT tools fit into the taxonomy is provided.

Testing the simulation capability of APSIM-ORYZA under different levels of nitrogen fertiliser and transplanting time regimes in Korea

APSIM-ORYZA is a new functionality developed in the APSIM framework to simulate rice production while addressing management issues such as fertilisation and transplanting, which are particularly important in Korean agriculture. To validate the model for Korean rice varieties and field conditions, the measured yields and flowering times from three field experiments conducted by the Gyeonggi Agricultural Research and Extension Services (GARES) in Korea were compared against the simulated outputs for different management practices and rice varieties. Simulated yields of early-, mid- and mid-to-late-maturing varieties of rice grown in a continuous rice cropping system from 1997 to 2004 showed close agreement with the measured data. Similar results were also found for yields simulated under seven levels of nitrogen application. When different transplanting times were modelled, simulated flowering times ranged from within 3 days of the measured values for the early-maturing varieties, to up to 9 days after the measured dates for the mid- and especially mid-to-late-maturing varieties. This was associated with highly variable simulated yields which correlated poorly with the measured data. This suggests the need to accurately calibrate the photoperiod sensitivity parameters of the model for the photoperiod-sensitive rice varieties in Korea.

Avaliação de sistemas de informações geográficas na internet através de teste de software.

A utilização de sistemas de informações geográficas via Web (Sigweb) tem crescido nos últimos anos pela facilidade na manipulação e visualização de informações de diferentes lugares através da Internet. O objetivo deste estudo é realizar testes de sistemas de informações geográficas na Internet com ênfase na técnica funcional para avaliar a funcionalidade, usabilidade, a navegabilidade dos programas conhecidos como Sigweb prontos para usar. Para tanto, foi necessária a identificação dos casos de uso dos programas propostos para o estudo. Como resultado se pode conferir o comportamento dos sistemas durante os testes, além de distinguir as características de cada SigWeb e as dificuldades encontradas.

The automation and evaluation of nested clade phylogeographic analysis

Nested clade phylogeographic analysis (NCPA) is a popular method for reconstructing the demographic history of spatially distributed populations from genetic data. Although some parts of the analysis are automated, there is no unique and widely followed algorithm for doing this in its entirety, beginning with the data, and ending with the inferences drawn from the data. This article describes a method that automates NCPA, thereby providing a framework for replicating analyses in an objective way. To do so, a number of decisions need to be made so that the automated implementation is representative of previous analyses. We review how the NCPA procedure has evolved since its inception and conclude that there is scope for some variability in the manual application of NCPA. We apply the automated software to three published datasets previously analyzed manually and replicate many details of the manual analyses, suggesting that the current algorithm is representative of how a typical user will perform NCPA. We simulate a large number of replicate datasets for geographically distributed, but entirely random-mating, populations. These are then analyzed using the automated NCPA algorithm. Results indicate that NCPA tends to give a high frequency of false positives. In our simulations we observe that 14% of the clades give a conclusive inference that a demographic event has occurred, and that 75% of the datasets have at least one clade that gives such an inference. This is mainly due to the generation of multiple statistics per clade, of which only one is required to be significant to apply the inference key. We survey the inferences that have been made in recent publications and show that the most commonly inferred processes (restricted gene flow with isolation by distance and contiguous range expansion) are those that are commonly inferred in our simulations. However, published datasets typically yield a richer set of inferences with NCPA than obtained in our random-mating simulations, and further testing of NCPA with models of structured populations is necessary to examine its accuracy.