Data Mining - Concepts and Techniques.

The main reason behind failure of lots of software is poor quality thus estimation software quality become an important task in software industry. An early estimation towards pre-released software quality plays an important role in increasing probability of project success. By analyzing the metric data we can forecast the quality of the object oriented system. Software metrics are necessary to identify where the resources are needed; they are a crucial source of information for decision making. This paper represents proposed model for estimation quality of software product.

A hierarchical model for object-oriented design quality assessment

https://zenodo.org/record/1997449/files/SS18.pdf

A survey on software smells

Over the past 15years, researchers presented numerous techniques and tools for mining code smells. It is imperative to classify, compare, and evaluate existing techniques and tools used for the detection of code smells because of their varying features and outcomes. This paper presents an up-to-date review on the state-of-the-art techniques and tools used for mining code smells from the source code of different software applications. We classify selected code smell detection techniques and tools based on their detection methods and analyze the results of the selected techniques. We present our observations and recommendations after our critical analysis of existing code smell techniques and tools. Our recommendations may be used by existing and new tool developers working in the field of code smell detection. The scope of this review is limited to research publications in the area of code smells that focus on detection of code smells as compared with previous reviews that cover all aspects of code smells. Copyright © 2015 John Wiley & Sons, Ltd.

A review of code smell mining techniques

River corridors play a crucial environmental, economic, and societal role yet also represent one of the world's most dangerous natural hazards, making monitoring imperative to improve our understanding and to protect people. Remote sensing offers a rapidly growing suite of methods by which river corridor monitoring can be performed efficiently, at a range of scales and in difficult environmental conditions. This paper aims to evaluate the current state and assess the potential future of river corridor monitoring, whilst highlighting areas that require further investigation. We initially review established methods that are used to undertake river corridor monitoring, framed by the context and scales upon which they are applied. Subsequently, we review cutting edge technologies that are being developed and focussed around unmanned aerial vehicle and multisensor system advances. We also “horizon scan” for future methods that may become increasingly prominent in research and management, citing examples from within and outside of the fluvial domain. Through review of the literature, it has become apparent that the main gap in fluvial remote sensing lies in the trade-off between resolution and scales. However, prioritising process measurements and simultaneous multisensor data collection is likely to offer a bigger advance in understanding than purely from better surveying methods alone. Challenges regarding the legal deployment of more complex systems, as well as effectively disseminating data into the science community, are amongst those that we propose need addressing. However, the plethora of methods currently available means that researchers and monitoring agencies will be able to identify suitable techniques for their needs.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/rra.3479

Remote sensing of river corridors: A review of current trends and future directions

A test-suite reduction approach to improving fault-localization effectiveness

The cooperative multi-targets assignment for multiple unmanned aerial vehicles (UAV) is a complex combinatorial optimization problem. Multi-UAVs cooperation increases the scale of problems which cause a noticeable increase in task planning time. Moreover, it is difficult to build a unified assignment model because different tasks often require different numbers of UAVs and targets. Besides, the cooperative constraints of multi-UAVs in a three-dimensional environments are more complex than that in a two-dimensional environments, which makes it difficult to obtain an optimal solution. To solve these problems, we present a unified gene coding strategy to handle various models in a consistent framework. Then, a cooperative target assignment algorithm in a three-dimensional environments based on discrete mapping differential evolution is given. First, we use flight path cost to indicate the assignment relationship between the UAV and the target, which turns the optimization problem from discrete space to continuous space, and so the solving process can be simplified. Secondly, in order to obtain reasonable offspring for differential evolution, we map the solution back to the assignment relationship space according to inverse mapping rules. Finally, to avoid falling into a local optimal, a balance between exploration and exploitation is achieved by combining the dynamic crossover rate with the hybrid evolution strategy. The simulation results show that the proposed discrete mapping differential evolution algorithm with the unified gene coding strategy not only effectively solves the cooperative multi-targets assignment problem, but also improves the accuracy of the multi-targets assignment. It is also suitable for solving the large scale problem of assignment.

Improved discrete mapping differential evolution for multi-unmanned aerial vehicles cooperative multi-targets assignment under unified model

Detecting bad smells in program design and implementation is a challenging task. Manual detection is proved to be time-consuming and inaccurate under complex situation. Weight based distance metrics and relevant conceptions are introduced in this paper, and the automatic approach for bad smells detection is proposed based on Jaccard distance. The conception of distance between entities and classes is defined and relevant computing formulas are applied in detecting. New weight based distance metrics theory is proposed to detect feature envy bad smell. This improved approach can express more detailed design quality and invoking relationship than the original distance metrics theory. With these improvements the automation of bad smells detection can be achieved with high accuracy. And then the approach is applied to detect bad smells in JFreeChart open source code. The experimental results show that the weight based distance metrics theory can detect the bad smell more accurately with low time complexity.

Detecting Bad Smells with Weight Based Distance Metrics Theory

AutoLEP, an automated learning and examination environment for programming is introduced. It differs from the other existing systems mainly in that it adopts a novel marking mechanism. It combines static analysis with dynamic testing to analyze students’ programs. So it can evaluate not only the testing results but also the constructs of the programs. It can give reasonable marks to programs with syntactical or logical errors. AutoLEP has been used in the C programming course since 2004 at Harbin Institute of Technology. The advantages of using AutoLEP are that the students are assessed on what they have to do in practice, rather than on their theoretical knowledge, and that the marking load on the examiner is drastically reduced.

AutoLEP: An Automated Learning and Examination System for Programming and its Application in Programming Course

Bad smells are signs of potential problems in code. Detecting bad smells, however, remains time consuming for software engineers despite proposals on bad smell detection and refactoring tools. Large Class is a kind of bad smells caused by large scale, and the detection is hard to achieve automatically. In this paper, a Large Class bad smell detection approach based on class length distribution model and cohesion metrics is proposed. In programs, the lengths of care confirmed according to the certain distributions. The class length distribution model is generalized to detect programs after grouping. Meanwhile, cohesion metrics are analyzed for bad smell detection. The bad smell detection experiments of open source programs show that Large Class bad smell can be detected effectively and accurately with this approach, and refactoring scheme can be proposed for design quality improvements of programs.

Ma Peijun

Papers

A test-suite reduction approach to improving fault-localization effectiveness

Improved discrete mapping differential evolution for multi-unmanned aerial vehicles cooperative multi-targets assignment under unified model

Detecting Bad Smells with Weight Based Distance Metrics Theory

AutoLEP: An Automated Learning and Examination System for Programming and its Application in Programming Course

Detection and refactoring of bad smell caused by large scale