Search results for: mining software repositories

Authors: Peter Sabev, Katalina Grigorova

Abstract:

Test automation allows performing difficult and time consuming manual software testing tasks efficiently, quickly and repeatedly. However, development and maintenance of automated tests is expensive, so it needs a proper prioritization what to automate first. This paper describes a simple yet efficient approach for such prioritization of test cases based on the effort needed for both manual execution and software test automation. The suggested approach is very flexible because it allows working with a variety of assessment methods, and adding or removing new candidates at any time. The theoretical ideas presented in this article have been successfully applied in real world situations in several software companies by the authors and their colleagues including testing of real estate websites, cryptographic and authentication solutions, OSGi-based middleware framework that has been applied in various systems for smart homes, connected cars, production plants, sensors, home appliances, car head units and engine control units (ECU), vending machines, medical devices, industry equipment and other devices that either contain or are connected to an embedded service gateway.

Keywords: Automated Testing, Manual Testing, Test Automation, Software testing, Test Prioritization.

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Authors: Yang Bao, LuJing Zhang

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Typical Intelligent Decision Support System is 4-based, its design composes of Data Warehouse, Online Analytical Processing, Data Mining and Decision Supporting based on models, which is called Decision Support System Based on Data Warehouse (DSSBDW). This way takes ETL,OLAP and DM as its implementing means, and integrates traditional model-driving DSS and data-driving DSS into a whole. For this kind of problem, this paper analyzes the DSSBDW architecture and DW model, and discusses the following key issues: ETL designing and Realization; metadata managing technology using XML; SQL implementing, optimizing performance, data mapping in OLAP; lastly, it illustrates the designing principle and method of DW in DSSBDW.

Keywords: Decision Support System, Data Warehouse, Data Mining.

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Authors: Paola Gattinoni, Laura Scesi, Elena Cerino Adbin, Daniele Cremonesi

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The interaction of tunneling or mining with groundwater has become a very relevant problem not only due to the need to guarantee the safety of workers and to assure the efficiency of the tunnel drainage systems, but also to safeguard water resources from impoverishment and pollution risk. Therefore it is very important to forecast the drainage processes (i.e., the evaluation of drained discharge and drawdown caused by the excavation). The aim of this study was to know better the system and to quantify the flow drained from the Fontane mines, located in Val Germanasca (Turin, Italy). This allowed to understand the hydrogeological local changes in time. The work has therefore been structured as follows: the reconstruction of the conceptual model with the geological, hydrogeological and geological-structural study; the calculation of the tunnel inflows (through the use of structural methods) and the comparison with the measured flow rates; the water balance at the basin scale. In this way it was possible to understand what are the relationships between rainfall, groundwater level variations and the effect of the presence of tunnels as a means of draining water. Subsequently, it the effects produced by the excavation of the mining tunnels was quantified, through numerical modeling. In particular, the modeling made it possible to observe the drawdown variation as a function of number, excavation depth and different mines linings.

Keywords: Groundwater, Italy, numerical model, tunneling.

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Authors: Sidharth Talan, Sanjay Kumar Sharma, Eoin Joseph Wallace, Nikita Agrawal

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Underground mining is at the core of rapidly evolving metals and minerals sector due to the increasing mineral consumption globally. Even though the surface mines are still more abundant on earth, the scales of industry are slowly tipping towards underground mining due to rising depth and complexities of orebodies. Thus, the efficient and productive functioning of underground operations depends significantly on the synchronized performance of key elements such as operating site, mining equipment, manpower and mine services. Production drilling is the process of conducting long hole drilling for the purpose of charging and blasting these holes for the production of ore in underground metal mines. Thus, production drilling is the crucial segment in the underground metal mining value chain. This paper presents the process mapping tool to evaluate the production drilling process in the underground metal mining operation by dividing the given process into three segments namely Input, Process and Output. The three segments are further segregated into factors and sub-factors. As per the study, the major input factors crucial for the efficient functioning of production drilling process are power, drilling water, geotechnical support of the drilling site, skilled drilling operators, services installation crew, oils and drill accessories for drilling machine, survey markings at drill site, proper housekeeping, regular maintenance of drill machine, suitable transportation for reaching the drilling site and finally proper ventilation. The major outputs for the production drilling process are ore, waste as a result of dilution, timely reporting and investigation of unsafe practices, optimized process time and finally well fragmented blasted material within specifications set by the mining company. The paper also exhibits the drilling loss matrix, which is utilized to appraise the loss in planned production meters per day in a mine on account of availability loss in the machine due to breakdowns, underutilization of the machine and productivity loss in the machine measured in drilling meters per unit of percussion hour with respect to its planned productivity for the day. The given three losses would be essential to detect the bottlenecks in the process map of production drilling operation so as to instigate the action plan to suppress or prevent the causes leading to the operational performance deficiency. The given tool is beneficial to mine management to focus on the critical factors negatively impacting the production drilling operation and design necessary operational and maintenance strategies to mitigate them. 

Keywords: Process map, drilling loss matrix, availability, utilization, productivity, percussion rate.

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Authors: Ryad Zemouri, Paul Ciprian Patic

Abstract:

An adaptive software reliability prediction model using evolutionary connectionist approach based on Recurrent Radial Basis Function architecture is proposed. Based on the currently available software failure time data, Fuzzy Min-Max algorithm is used to globally optimize the number of the k Gaussian nodes. The corresponding optimized neural network architecture is iteratively and dynamically reconfigured in real-time as new actual failure time data arrives. The performance of our proposed approach has been tested using sixteen real-time software failure data. Numerical results show that our proposed approach is robust across different software projects, and has a better performance with respect to next-steppredictability compared to existing neural network model for failure time prediction.

Keywords: Neural network, Prediction error, Recurrent RadialBasis Function Network, Reliability prediction.

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Authors: Zine-Eddine Bouras, Abdelouaheb Talai

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The need to merge software artifacts seems inherent to modern software development. Distribution of development over several teams and breaking tasks into smaller, more manageable pieces are an effective means to deal with the kind of complexity. In each case, the separately developed artifacts need to be assembled as efficiently as possible into a consistent whole in which the parts still function as described. In addition, earlier changes are introduced into the life cycle and easier is their management by designers. Interaction-based specifications such as UML sequence diagrams have been found effective in this regard. As a result, sequence diagrams can be used not only for capturing system behaviors but also for merging changes in order to create a new version. The objective of this paper is to suggest a new approach to deal with the problem of software merging at the level of sequence diagrams by using the concept of dependence analysis that captures, formally, all mapping, and differences between elements of sequence diagrams and serves as a key concept to create a new version of sequence diagram.

Keywords: System behaviors, sequence diagram merging, dependence analysis, sequence diagram slicing.

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Authors: Lu Zou, Dianfu MA, Ying Wang, Xianqi Zhao

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Avionic software architecture has transit from a federated avionics architecture to an integrated modular avionics (IMA) .ARINC 653 (Avionics Application Standard Software Interface) is a software specification for space and time partitioning in Safety-critical avionics Real-time operating systems. Methods to transform the abstract avionics application logic function to the executable model have been brought up, however with less consideration about the code generating input and output model specific for ARINC 653 platform and inner-task synchronous dynamic interaction order sequence. In this paper, we proposed an AADL-based model-driven design methodology to fulfill the purpose to automatically generating Cµ executable model on ARINC 653 platform from the ARINC653 architecture which defined as AADL653 in order to facilitate the development of the avionics software constructed on ARINC653 OS. This paper presents the mapping rules between the AADL653 elements and the elements in Cµ language, and define the code generating rules , designs an automatic C µ code generator .Then, we use a case to illustrate our approach. Finally, we give the related work and future research directions.

Keywords: IMA, ARINC653, AADL653, code generation.

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Authors: A. Faro, D. Giordano, F. Maiorana

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Data mining uses a variety of techniques each of which is useful for some particular task. It is important to have a deep understanding of each technique and be able to perform sophisticated analysis. In this article we describe a tool built to simulate a variation of the Kohonen network to perform unsupervised clustering and support the entire data mining process up to results visualization. A graphical representation helps the user to find out a strategy to optimize classification by adding, moving or delete a neuron in order to change the number of classes. The tool is able to automatically suggest a strategy to optimize the number of classes optimization, but also support both tree classifications and semi-lattice organizations of the classes to give to the users the possibility of passing from one class to the ones with which it has some aspects in common. Examples of using tree and semi-lattice classifications are given to illustrate advantages and problems. The tool is applied to classify macroeconomic data that report the most developed countries- import and export. It is possible to classify the countries based on their economic behaviour and use the tool to characterize the commercial behaviour of a country in a selected class from the analysis of positive and negative features that contribute to classes formation. Possible interrelationships between the classes and their meaning are also discussed.

Keywords: Unsupervised classification, Kohonen networks, macroeconomics, Visual data mining, Cluster interpretation.

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Authors: Pei Lin Khoo, Yunli Lee

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Weblogs are resource of social structure to discover and track the various type of information written by blogger. In this paper, we proposed to use mining weblogs technique for identifying the trends of influenza where blogger had disseminated their opinion for the anomaly disease. In order to identify the trends, web crawler is applied to perform a search and generated a list of visited links based on a set of influenza keywords. This information is used to implement the analytics report system for monitoring and analyzing the pattern and trends of influenza (H1N1). Statistical and graphical analysis reports are generated. Both types of the report have shown satisfactory reports that reflect the awareness of Malaysian on the issue of influenza outbreak through blogs.

Keywords: H1N1, Weblogs, Web Crawler, Analytics Report System.

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Authors: N. Mantalay, N. Chakpitak, W. Janchai, P. Sureepong

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Work stress causes the organizational work-life imbalance of employees. Because of this imbalance, workers perform with lower effort to finish assignments and thus an organization will experience reduced productivity. In order to investigate the problem of an organizational work-life imbalance, this qualitative case study focuses on an organizational work-life imbalance among Thai software developers in a German-owned company in Chiang Mai, Thailand. In terms of knowledge management, fishbone diagram is useful analysis tool to investigate the root causes of an organizational work-life imbalance systematically in focus-group discussions. Furthermore, fishbone diagram shows the relationship between causes and effects clearly. It was found that an organizational worklife imbalance among Thai software developers is influenced by management team, work environment, and information tools used in the company over time.

Keywords: knowledge management, knowledge worker, worklife imbalance, fishbone diagram

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Authors: Tomáš Seidler, Marek Mihola, Denisa Cihlarova

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The article presents analysis results of maps of expected subsidence in undermined areas for road repair management. The analysis was done in the area of Karvina district in the Czech Republic, including undermined areas with ongoing deep mining activities or finished deep mining in years 2003 - 2009. The article discusses the possibilities of local road maintenance authorities to determine areas that will need most repairs in the future with limited data available. Using the expected subsidence maps new map of surface curvature was calculated. Combined with road maps and historical data about repairs the result came for five main categories of undermined areas, proving very simple tool for management.

Keywords: GIS, Map of Subsidence, Road, Undermined Area

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Authors: M. Sedighizadeh, A. Rezazadeh

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A method has been developed for preparing load models for power flow and stability. The load modeling (LOADMOD) computer software transforms data on load class mix, composition, and characteristics into the from required for commonly–used power flow and transient stability simulation programs. Typical default data have been developed for load composition and characteristics. This paper defines LOADMOD software and describes the dynamic and static load modeling techniques used in this software and results of initial testing for BAKHTAR power system.

Keywords: Load Modelling, Static, Power Flow.

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Authors: Parvinder S. Sandhu, Roma Jaswal, Sandeep Khimta, Shailendra Singh

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As the majority of faults are found in a few of its modules so there is a need to investigate the modules that are affected severely as compared to other modules and proper maintenance need to be done in time especially for the critical applications. As, Neural networks, which have been already applied in software engineering applications to build reliability growth models predict the gross change or reusability metrics. Neural networks are non-linear sophisticated modeling techniques that are able to model complex functions. Neural network techniques are used when exact nature of input and outputs is not known. A key feature is that they learn the relationship between input and output through training. In this present work, various Neural Network Based techniques are explored and comparative analysis is performed for the prediction of level of need of maintenance by predicting level severity of faults present in NASA-s public domain defect dataset. The comparison of different algorithms is made on the basis of Mean Absolute Error, Root Mean Square Error and Accuracy Values. It is concluded that Generalized Regression Networks is the best algorithm for classification of the software components into different level of severity of impact of the faults. The algorithm can be used to develop model that can be used for identifying modules that are heavily affected by the faults.

Keywords: Neural Network, Software faults, Software Metric.

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Authors: Kostyantyn Yermashov, Karsten Wolke, Karl Hayo Siemsen

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Domain-specific languages describe specific solutions to problems in the application domain. Traditionally they form a solution composing black-box abstractions together. This, usually, involves non-deep transformations over the target model. In this paper we argue that it is potentially powerful to operate with grey-box abstractions to build a domain-specific software system. We present parametric code templates as grey-box abstractions and conceptual tools to encapsulate and manipulate these templates. Manipulations introduce template-s merging routines and can be defined in a generic way. This involves reasoning mechanisms at the code templates level. We introduce the concept of Neurath Modelling Language (NML) that operates with parametric code templates and specifies a visualisation mapping mechanism for target models. Finally we provide an example of calculating a domain-specific software system with predefined NML elements.

Keywords: software design, code templates, domain-specific languages, modelling languages, generic tools

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Authors: Lívia B. Meirelles, Erika C. A. N. Chrisman, Flávia B. de Andrade, Lilian C. M. de Oliveira

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True Boiling Point distillation (TBP) is one of the most common experimental techniques for the determination of petroleum properties. This curve provides information about the performance of petroleum in terms of its cuts. The experiment is performed in a few days. Techniques are used to determine the properties faster with a software that calculates the distillation curve when a little information about crude oil is known. In order to evaluate the accuracy of distillation curve prediction, eight points of the TBP curve and specific gravity curve (348 K and 523 K) were inserted into the HYSYS Oil Manager, and the extended curve was evaluated up to 748 K. The methods were able to predict the curve with the accuracy of 0.6%-9.2% error (Software X ASTM), 0.2%-5.1% error (Software X Spaltrohr).

Keywords: Distillation curve, petroleum distillation, simulation, true boiling point curve.

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Authors: Syahanim Mohd Salleh, Zaihosnita Hood, Hairulliza Mohd Judi, Marini Abu Bakar

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CFlow is a flow chart software, it contains facilities to draw and evaluate a flow chart. A flow chart evaluation applies a simulation method to enable presentation of work flow in a flow chart solution. Flow chart simulation of CFlow is executed by manipulating the CFlow data file which is saved in a graphical vector format. These text-based data are organised by using a data classification technic based on a Library classification-scheme. This paper describes the file format for flow chart simulation software of CFlow.

Keywords: CFlow, flow chart, file format.

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Authors: Jagdeep Kaur, Parvinder S. Sandhu, Birinderjit Singh, Amit Verma, Sanyam Anand

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In today-s hip hop world where everyone is running short of time and works hap hazardly,the similar scene is common on the roads while in traffic.To do away with the fatal consequences of such speedy traffics on rushy lanes, a software to analyse and keep account of the traffic and subsequent conjestion is being used in the developed countries. This software has being implemented and used with the help of a suppprt tool called Critical Analysis Reporting Environment.There has been two existing versions of this tool.The current research paper involves examining the issues and probles while using these two practically. Further a hybrid architecture is proposed for the same that retains the quality and performance of both and is better in terms of coupling of components , maintainence and many other features.

Keywords: Critical Analysis Reporting Environment, coupling, hybrid architecture etc.

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Authors: Ramandeep S. Sidhu, Sunil Khullar, Parvinder S. Sandhu, R. P. S. Bedi, Kiranbir Kaur

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In this paper, subtractive clustering based fuzzy inference system approach is used for early detection of faults in the function oriented software systems. This approach has been tested with real time defect datasets of NASA software projects named as PC1 and CM1. Both the code based model and joined model (combination of the requirement and code based metrics) of the datasets are used for training and testing of the proposed approach. The performance of the models is recorded in terms of Accuracy, MAE and RMSE values. The performance of the proposed approach is better in case of Joined Model. As evidenced from the results obtained it can be concluded that Clustering and fuzzy logic together provide a simple yet powerful means to model the earlier detection of faults in the function oriented software systems.

Keywords: Subtractive clustering, fuzzy inference system, fault proneness.

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Authors: Prasanth Sabarish Nair, Thomas Binu, Madiajagan Muthaiyan

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This paper discusses a software application to aid in the smooth functioning of a university internship program, including a student, faculty and an administration module. The software can also calculate the most apt combination of students to stations and allocate them respectively.

Keywords: Academic evaluation, administration monitoring, automatic allocation system, internship, student preferences.

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Authors: Chandrika M.J., Lariyah M.S., Alicia Y.C. Tang

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Ontology is widely being used as a tool for organizing information, creating the relation between the subjects within the defined knowledge domain area. Various fields such as Civil, Biology, and Management have successful integrated ontology in decision support systems for managing domain knowledge and to assist their decision makers. Gross pollutant traps (GPT) are devices used in trapping and preventing large items or hazardous particles in polluting and entering our waterways. However choosing and determining GPT is a challenge in Malaysia as there are inadequate GPT data repositories being captured and shared. Hence ontology is needed to capture, organize and represent this knowledge into meaningful information which can be contributed to the efficiency of GPT selection in Malaysia urbanization. A GPT Ontology framework is therefore built as the first step to capture GPT knowledge which will then be integrated into the decision support system. This paper will provide several examples of the GPT ontology, and explain how it is constructed by using the Protégé tool.

Keywords: Gross pollutant Trap, Ontology, Protégé.

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Authors: A. Ali, V. Strezov, P. Davies, I. Wright, T. Kan

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The environmental impacts arising from mining activities affect the air, water, and soil quality. Impacts may result in unexpected and adverse environmental outcomes. This study reports on the impact of coal production on sediment in Sydney region of Australia. The sediment samples upstream and downstream from the discharge points from three mines were taken, and 80 parameters were tested. The results were assessed against sediment quality based on presence of metals. The study revealed the increment of metal content in the sediment downstream of the reference locations. In many cases, the sediment was above the Australia and New Zealand Environment Conservation Council and international sediment quality guidelines value (SQGV). The major outliers to the guidelines were nickel (Ni) and zinc (Zn).

Keywords: Coal mine, environmental impact, produced water, sediment quality guidelines value.

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Authors: Karel Frajtak, Miroslav Bures, Ivan Jelinek

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Software testing is important stage of software development cycle. Current testing process involves tester and electronic documents with test case scenarios. In this paper we focus on new approach to testing process using automated test case generation and tester guidance through the system based on the model of the system. Test case generation and model-based testing is not possible without proper system model. We aim on providing better feedback from the testing process thus eliminating the unnecessary paper work.

Keywords: Model based testing, test automation, test generating, tester support.

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Authors: Nasrin Farajiparvar

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In the present article, a new method has been developed to enhance the application of equipment monitoring, which in turn results in improving condition-based maintenance economic impact in an automobile parts manufacturing factory. This study also describes how an effective software with a simple database can be utilized to achieve cost-effective improvements in maintenance performance. The most important results of this project are indicated here: 1. 63% reduction in direct and indirect maintenance costs. 2. Creating a proper database to analyse failures. 3. Creating a method to control system performance and develop it to similar systems. 4. Designing a software to analyse database and consequently create technical knowledge to face unusual condition of the system. Moreover, the results of this study have shown that the concept and philosophy of maintenance has not been understood in most Iranian industries. Thus, more investment is strongly required to improve maintenance conditions.

Keywords: Condition-based maintenance, Economic savings, Iran industries, Machine life prediction software.

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Authors: P. H. Mukti, I. Kurniawati, F. Oktaviansyah, A. D. Adhitya, N. Rachmadani, R. Corputty, G. Hendrantoro, T. Fukusako

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HF Communication system is one of the attractive fields among many researchers since it can be reached long-distance areas with low-cost. This long-distance communication can be achieved by exploiting the ionosphere as a transmission medium for the HF radio wave. However, due to the dynamic nature of ionosphere, the channel characteristic of HF communication has to be investigated in order to gives better performances. Many techniques to characterize HF channel are available in the literature. However, none of those techniques describe the HF channel characteristic in low-latitude regions, especially equatorial areas. Since the ionosphere around equatorial region has an ESF phenomenon, it becomes an important investigation to characterize the wideband HF Channel in low-latitude region. On the other sides, the appearance of software-defined radio attracts the interest of many researchers. Accordingly, in this paper a SDR-based channel measurement system is proposed to be used for characterizing the HF channel in low-latitude region.

Keywords: Channel Characteristic, HF Communication System, LabVIEW, Software-Defined Radio, Universal Software Radio Pheripheral.

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Authors: Aitana Alonso-Nogueira, Helia Estévez-Fernández, Isaías García

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This paper presents an approach to reduce some of its current flaws in the requirements phase inside the software development process. It takes the software requirements of an application, makes a conceptual modeling about it and formalizes it within JSON documents. This formal model is lodged in a NoSQL database which is document-oriented, that is, MongoDB, because of its advantages in flexibility and efficiency. In addition, this paper underlines the contributions of the detailed approach and shows some applications and benefits for the future work in the field of automatic code generation using model-driven engineering tools.

Keywords: Conceptual modeling, JSON, NoSQL databases, requirements engineering, software development.

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Authors: Farhan Ismaeel Dakheel, Raoof Smko, K. Negrat, Abdelsalam Almarimi

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In this paper we used data mining techniques to identify outlier patients who are using large amount of drugs over a long period of time. Any healthcare or health insurance system should deal with the quantities of drugs utilized by chronic diseases patients. In Kingdom of Bahrain, about 20% of health budget is spent on medications. For the managers of healthcare systems, there is no enough information about the ways of drug utilization by chronic diseases patients, is there any misuse or is there outliers patients. In this work, which has been done in cooperation with information department in the Bahrain Defence Force hospital; we select the data for Cardiac patients in the period starting from 1/1/2008 to December 31/12/2008 to be the data for the model in this paper. We used three techniques for finding the drug utilization for cardiac patients. First we applied a clustering technique, followed by measuring of clustering validity, and finally we applied a decision tree as classification algorithm. The clustering results is divided into three clusters according to the drug utilization, for 1603 patients, who received 15,806 prescriptions during this period can be partitioned into three groups, where 23 patients (2.59%) who received 1316 prescriptions (8.32%) are classified to be outliers. The classification algorithm shows that the use of average drug utilization and the age, and the gender of the patient can be considered to be the main predictive factors in the induced model.

Keywords: Data Mining, Clustering, Classification, Drug Utilization..

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Authors: Ruchika Malhotra, Megha Khanna

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The development of change prediction models can help the software practitioners in planning testing and inspection resources at early phases of software development. However, a major challenge faced during the training process of any classification model is the imbalanced nature of the software quality data. A data with very few minority outcome categories leads to inefficient learning process and a classification model developed from the imbalanced data generally does not predict these minority categories correctly. Thus, for a given dataset, a minority of classes may be change prone whereas a majority of classes may be non-change prone. This study explores various alternatives for adeptly handling the imbalanced software quality data using different sampling methods and effective MetaCost learners. The study also analyzes and justifies the use of different performance metrics while dealing with the imbalanced data. In order to empirically validate different alternatives, the study uses change data from three application packages of open-source Android data set and evaluates the performance of six different machine learning techniques. The results of the study indicate extensive improvement in the performance of the classification models when using resampling method and robust performance measures.

Keywords: Change proneness, empirical validation, imbalanced learning, machine learning techniques, object-oriented metrics.

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Authors: Torsti Rantapuska, Sariseelia Sore

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Acquiring commercial off-the-shelf (COTS) software applications is becoming routine in organizations. However, eliciting user requirements, finding the candidate COTS products and making the decision is a complex task, especially for SMEs who do not have the time and knowledge needed to do the task properly. The existing models intended to help the decision makers are originally designed for professional use. SMEs are obligated to rely on the software vendor’s ability to solve the problem with the systems provided.  In this paper, we develop a model for SMEs for the acquisition of Commercial Off-The-Shelf (COTS) software products. A leading idea of the model is that the ICT investment is basically a change initiative and therefore it should also be taken as a process of organizational learning. The model is designed bearing three objectives in mind: 1) business orientation, 2) agility, and 3) Learning and knowledge management orientation. The model can be applied to ICT investments in SMEs which have a professional team leader with basic business and IT knowledge. 

 

Keywords: COTS acquisition, ICT investment, organizational learning, ICT adoption.

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Authors: Jihyun Song, Kyeongjoo Kim, Minsoo Lee

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Due to the development of information technology and wireless Internet technology, various data are being generated in various fields. These data are advantageous in that they provide real-time information to the users themselves. However, when the data are accumulated and analyzed, more various information can be extracted. In addition, development and dissemination of boards such as Arduino and Raspberry Pie have made it possible to easily test various sensors, and it is possible to collect sensor data directly by using database application tools such as MySQL. These directly collected data can be used for various research and can be useful as data for data mining. However, there are many difficulties in using the board to collect data, and there are many difficulties in using it when the user is not a computer programmer, or when using it for the first time. Even if data are collected, lack of expert knowledge or experience may cause difficulties in data analysis and visualization. In this paper, we aim to construct a library for sensor data collection and analysis to overcome these problems.

Keywords: Clustering, data mining, DBSCAN, k-means, k-medoids, sensor data.

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Authors: Cherry Oo, Hnin Min Oo

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Software bug localization is one of the most costly tasks in program repair technique. Therefore, there is a high claim for automated bug localization techniques that can monitor programmers to the locations of bugs, with slight human arbitration. Spectrum-based bug localization aims to help software developers to discover bugs rapidly by investigating abstractions of the program traces to make a ranking list of most possible buggy modules. Using the Apache Commons Math library project, we study the diagnostic accuracy using our spectrum-based bug localization metric. Our outcomes show that the greater performance of a specific similarity coefficient, used to inspect the program spectra, is mostly effective on localizing of single line bugs.

Keywords: Software testing, fault localization, program spectra.

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