Architecture aware parallel programming in Glasgow parallel Haskell (GPH)

General purpose computing architectures are evolving quickly to become manycore and hierarchical: i.e. a core can communicate more quickly locally than globally. To be effective on such architectures, programming models must be aware of the communications hierarchy. This thesis investigates a programming model that aims to share the responsibility of task placement, load balance, thread creation, and synchronisation between the application developer and the runtime system. The main contribution of this thesis is the development of four new architectureaware constructs for Glasgow parallel Haskell that exploit information about task size and aim to reduce communication for small tasks, preserve data locality, or to distribute large units of work. We define a semantics for the constructs that specifies the sets of PEs that each construct identifies, and we check four properties of the semantics using QuickCheck. We report a preliminary investigation of architecture aware programming models that abstract over the new constructs. In particular, we propose architecture aware evaluation strategies and skeletons. We investigate three common paradigms, such as data parallelism, divide-and-conquer and nested parallelism, on hierarchical architectures with up to 224 cores. The results show that the architecture-aware programming model consistently delivers better speedup and scalability than existing constructs, together with a dramatic reduction in the execution time variability. We present a comparison of functional multicore technologies and it reports some of the first ever multicore results for the Feedback Directed Implicit Parallelism (FDIP) and the semi-explicit parallelism (GpH and Eden) languages. The comparison reflects the growing maturity of the field by systematically evaluating four parallel Haskell implementations on a common multicore architecture. The comparison contrasts the programming effort each language requires with the parallel performance delivered. We investigate the minimum thread granularity required to achieve satisfactory performance for three implementations parallel functional language on a multicore platform. The results show that GHC-GUM requires a larger thread granularity than Eden and GHC-SMP. The thread granularity rises as the number of cores rises

Automated metamorphic testing on the analyses of feature models

Copyright © 2010 Elsevier B.V. All rights reserved.Context: A feature model (FM) represents the valid combinations of features in a domain. The automated extraction of information from FMs is a complex task that involves numerous analysis operations, techniques and tools. Current testing methods in this context are manual and rely on the ability of the tester to decide whether the output of an analysis is correct. However, this is acknowledged to be time-consuming, error-prone and in most cases infeasible due to the combinatorial complexity of the analyses, this is known as the oracle problem.Objective: In this paper, we propose using metamorphic testing to automate the generation of test data for feature model analysis tools overcoming the oracle problem. An automated test data generator is presented and evaluated to show the feasibility of our approach.Method: We present a set of relations (so-called metamorphic relations) between input FMs and the set of products they represent. Based on these relations and given a FM and its known set of products, a set of neighbouring FMs together with their corresponding set of products are automatically generated and used for testing multiple analyses. Complex FMs representing millions of products can be efficiently created by applying this process iteratively.Results: Our evaluation results using mutation testing and real faults reveal that most faults can be automatically detected within a few seconds. Two defects were found in FaMa and another two in SPLOT, two real tools for the automated analysis of feature models. Also, we show how our generator outperforms a related manual suite for the automated analysis of feature models and how this suite can be used to guide the automated generation of test cases obtaining important gains in efficiency.Conclusion: Our results show that the application of metamorphic testing in the domain of automated analysis of feature models is efficient and effective in detecting most faults in a few seconds without the need for a human oracle.This work has been partially supported by the European Commission(FEDER)and Spanish Government under CICYT project SETI(TIN2009-07366)and the Andalusian Government project ISABEL(TIC-2533)

Software estimating: a description and analysis of current methodologies with recommendations on appropriate techniques for estimating RIT research corporation software projects

This thesis investigated, described, and analyzed six current software estimation methodologies. Included were Boehm\u27s COCOMO, Esterling\u27s Work Productivity Model, Putnam\u27s Life Cycle Model and Albrecht\u27s Function Point Determination. An implementation strategy for software estimation within RIT Research Corporation, a consulting firm, has been developed, based on this analysis. This strategy attempts to satisfy key needs and problems encountered by RIT Research estimators, while providing cost effective, accurate estimates

Efficient Concept Drift Handling for Batch Android Malware Detection Models

The rapidly evolving nature of Android apps poses a significant challenge to static batch machine learning algorithms employed in malware detection systems, as they quickly become obsolete. Despite this challenge, the existing literature pays limited attention to addressing this issue, with many advanced Android malware detection approaches, such as Drebin, DroidDet and MaMaDroid, relying on static models. In this work, we show how retraining techniques are able to maintain detector capabilities over time. Particularly, we analyze the effect of two aspects in the efficiency and performance of the detectors: 1) the frequency with which the models are retrained, and 2) the data used for retraining. In the first experiment, we compare periodic retraining with a more advanced concept drift detection method that triggers retraining only when necessary. In the second experiment, we analyze sampling methods to reduce the amount of data used to retrain models. Specifically, we compare fixed sized windows of recent data and state-of-the-art active learning methods that select those apps that help keep the training dataset small but diverse. Our experiments show that concept drift detection and sample selection mechanisms result in very efficient retraining strategies which can be successfully used to maintain the performance of the static Android malware state-of-the-art detectors in changing environments.Comment: 18 page

Quality Evaluation of Requirements Models: The Case of Goal Models and Scenarios

Context: Requirements Engineering approaches provide expressive model techniques for requirements elicitation and analysis. Yet, these approaches struggle to manage the quality of their models, causing difficulties in understanding requirements, and increase development costs. The models’ quality should be a permanent concern. Objectives: We propose a mixed-method process for the quantitative evaluation of the quality of requirements models and their modelling activities. We applied the process to goal-oriented (i* 1.0 and iStar 2.0) and scenario-based (ARNE and ALCO use case templates) models, to evaluate their usability in terms of appropriateness recognisability and learnability. We defined (bio)metrics about the models and the way stakeholders interact with them, with the GQM approach. Methods: The (bio)metrics were evaluated through a family of 16 quasi-experiments with a total of 660 participants. They performed creation, modification, understanding, and review tasks on the models. We measured their accuracy, speed, and ease, using metrics of task success, time, and effort, collected with eye-tracking, electroencephalography and electro-dermal activity, and participants’ opinion, through NASA-TLX. We characterised the participants with GenderMag, a method for evaluating usability with a focus on gender-inclusiveness. Results: For i*, participants had better performance and lower effort when using iStar 2.0, and produced models with lower accidental complexity. For use cases, participants had better performance and lower effort when using ALCO. Participants using a textual representation of requirements had higher performance and lower effort. The results were better for ALCO, followed by ARNE, iStar 2.0, and i* 1.0. Participants with a comprehensive information processing and a conservative attitude towards risk (characteristics that are frequently seen in females) took longer to start the tasks but had a higher accuracy. The visual and mental effort was also higher for these participants. Conclusions: A mixed-method process, with (bio)metric measurements, can provide reliable quantitative information about the success and effort of a stakeholder while working on different requirements models’ tasks