Predicting glaucomatous visual field deterioration through short multivariate time series modelling

In bio-medical domains there are many applications involving the modelling of multivariate time series (MTS) data. One area that has been largely overlooked so far is the particular type of time series where the data set consists of a large number of variables but with a small number of observations. In this paper we describe the development of a novel computational method based on genetic algorithms that bypasses the size restrictions of traditional statistical MTS methods, makes no distribution assumptions, and also locates the order and associated parameters as a whole step. We apply this method to the prediction and modelling of glaucomatous visual field deterioration

Comprehension of object-oriented software cohesion: The empirical quagmire

Chidamber and Kemerer (1991) proposed an object-oriented (OO) metric suite which included the Lack of Cohesion Of Methods (LCOM) metric. Despite considerable effort both theoretically and empirically since then, the software engineering community is still no nearer finding a generally accepted definition or measure of OO cohesion. Yet, achieving highly cohesive software is a cornerstone of software comprehension and hence, maintainability. In this paper, we suggest a number of suppositions as to why a definition has eluded (and we feel will continue to elude) us. We support these suppositions with empirical evidence from three large C++ systems and a cohesion metric based on the parameters of the class methods; we also draw from other related work. Two major conclusions emerge from the study. Firstly, any sensible cohesion metric does at least provide insight into the features of the systems being analysed. Secondly however, and less reassuringly, the deeper the investigative search for a definitive measure of cohesion, the more problematic its understanding becomes; this casts serious doubt on the use of cohesion as a meaningful feature of object-orientation and its viability as a tool for software comprehension

A search-based technique for testing from extended finite state machine model

Extended finite state machines (EFSMs), and languages such as state-charts that are similar to EFSMs, are widely used to model state-based systems. When testing from an EFSM M it is common to aim to produce a set of test sequences (input sequences) that satisfies a test criterion that relates to the transition paths (TPs) of M that are executed by the test sequences. For example, we might require that the set of TPs triggered includes all of the transitions of M. One approach to generating such a set of test sequences is to split the problem into two stages: choosing a set of TPs that achieves the test criterion and then producing test sequences to trigger these TPs. However, the EFSM may contain infeasible TPs and the problem of generating a test sequence to trigger a given feasible TP (FTP) is generally uncomputable. In this paper we present a search-based approach that uses two techniques: (1) A TP fitness metric based on our previous work that estimates the feasibility of a given transition path; and (2) A fitness function to guide the search for a test sequence to trigger a given FTP. We evaluated our approach on five EFSMs: A simple in-flight safety system; a class II transport protocol; a lift system; an ATM; and the Inres initiator. In the experiments the proposed approach successfully tested approximately 96.75 % of the transitions and the proposed test sequence generation technique triggered all of the generated FTPs

Generating feasible transition paths for testing from an extended finite state machine (EFSM)

The problem of testing from an extended finite state machine (EFSM) can be expressed in terms of finding suitable paths through the EFSM and then deriving test data to follow the paths. A chosen path may be infeasible and so it is desirable to have methods that can direct the search for appropriate paths through the EFSM towards those that are likely to be feasible. However, generating feasible transition paths (FTPs) for model based testing is a challenging task and is an open research problem. This paper introduces a novel fitness metric that analyzes data flow dependence among the actions and conditions of the transitions in order to estimate the feasibility of a transition path. The proposed fitness metric is evaluated by being used in a genetic algorithm to guide the search for FTPs

Variable grouping in multivariate time series via correlation

The decomposition of high-dimensional multivariate time series (MTS) into a number of low-dimensional MTS is a useful but challenging task because the number of possible dependencies between variables is likely to be huge. This paper is about a systematic study of the “variable groupings” problem in MTS. In particular, we investigate different methods of utilizing the information regarding correlations among MTS variables. This type of method does not appear to have been studied before. In all, 15 methods are suggested and applied to six datasets where there are identifiable mixed groupings of MTS variables. This paper describes the general methodology, reports extensive experimental results, and concludes with useful insights on the strength and weakness of this type of grouping metho

Automatic generation of test sequences form EFSM models using evolutionary algorithms

Automated test data generation through evolutionary testing (ET) is a topic of interest to the software engineering community. While there are many ET-based techniques for automatically generating test data from code, the problem of generating test data from an extended finite state machine (EFSMs) is more complex and has received little attention. In this paper, we introduce a novel approach that addresses the problem of generating input test sequences that trigger given feasible paths in an EFSM model by employing an ET-based technique. The proposed approach expresses the problem as a search for input parameters to be applied to a set of functions to be called sequentially. In order to apply ET-based technique, a new fitness function is introduced to cope with the case when a test target involves calls to a set of transitions sequentially. We evaluate our approach empirically using five sets of randomly generated paths through two EFSM case studies: INRES and class 2 transport protocols. In the experiments, we apply two search techniques: a random and an ET-based which utilizes our new fitness function. Experimental results show that the proposed approach produces input test sequences that trigger all the feasible paths used with a success rate of 100%, however, the random technique failed in most cases with a success rate of 20.8%

A testability transformation approach for state-based programs

Search based testing approaches are efficient in test data generation; however they are likely to perform poorly when applied to programs with state variables. The problem arises when the target function includes guards that reference some of the program state variables whose values depend on previous function calls. Thus, merely considering the target function to derive test data is not sufficient. This paper introduces a testability transformation approach based on the analysis of control and data flow dependencies to bypass the state variable problem. It achieves this by eliminating state variables from guards and/ or determining which functions to call in order to satisfy guards with state variables. A number of experiments demonstrate the value of the proposed approach

Generating feasible transition paths for testing from an extended finite state machine (EFSM) with the counter problem

The extended finite state machine (EFSM) is a powerful approach for modeling state-based systems. However, testing from EFSMs is complicated by the existence of infeasible paths. One important problem is the existence of a transition with a guard that references a counter variable whose value depends on previous transitions. The presence of such transitions in paths often leads to infeasible paths. This paper proposes a novel approach to bypass the counter problem. The proposed approach is evaluated by being used in a genetic algorithm to guide the search for feasible transition paths (FTPs)

The detection and classification of blast cell in Leukaemia Acute Promyelocytic Leukaemia (AML M3) blood using simulated annealing and neural networks

This paper was delivered at AIME 2011: 13th Conference on Artifical Intelligence in Medicine.This paper presents a method for the detection and classification of blast cells in M3 with others sub-types using simulated annealing and neural networks. In this paper, we increased our test result from 10 images to 20 images. We performed Hill Climbing, Simulated Annealing and Genetic Algorithms for detecting the blast cells. As a result, simulated annealing is the “best” heuristic search for detecting the leukaemia cells. From the detection, we performed features extraction on the blast cells and we classifying based on M3 and other sub-types using neural networks. We received convincing result which has targeting around 97% in classifying of M3 with other sub-types. Our results are based on real world image data from a Haematology Department.Universiti Sains Islam Malaysia and the Ministry of Higher Education, Malaysi