Software Development,Android projects and apps,website Desining: May 2014

Wednesday, 28 May 2014

M.Phil Thesis at chennai

Gagner Technologies offers M.E Projects based on IEEE 2013. Final Year Projects, M.E Projects 2014-2015, mini Projects 2014-2015, Real Time Projects, Final Year Projects for BE ECE, CSE, IT, MCA, B TECH, ME, M SC (IT), BCA, BSC CSE, IT IEEE 2014 Projects in Data Mining, Distributed System, Mobile Computing, Networks, Networking. IEEE 2014 - 2015 Projects. Final Year Projects at Chennai, IEEE Software Projects, Engineering Projects, MCA Projects, BE Projects, JAVA Projects, J2EE Projects, .NET Projects, Students Projects, Final Year Student Projects, IEEE Projects 2014-2015, Real Time Projects, Final Year Projects for BE ECE, CSE, IT, MCA, B TECH, ME, M SC (IT), BCA, BSC CSE, IT, software Engineering

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Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

software engineering Projects in chennai

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Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

Network security projects in chennai

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Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

Networking projects in chennai

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Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

Distributed system Project in chennau

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Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

mobile computing projects in chennai

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Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

Cloud computing Projects in chennai

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Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

Data Mining Projects In chennai

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Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

IEEE 2014 Projects in chennai

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Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

Monday, 26 May 2014

Mini Projects for final year

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Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),

T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

Android project for final year students

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Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),

T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

Mechanical mini projects for final year M.E Students

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Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),

T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

Mechanical Mini project for final year M.Tech

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Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),

T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

Mechanical mini projects for final year B.E

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Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),

T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

mechanical mini projects for final year B.Tech

For more details Contact:

Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),

T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

Mechanical mini project for final year students

For more details Contact:

Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),

T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

Android mini project for final year Mca

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Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),

T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

Android mini project for msc computer science

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Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),

T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

Android project for final year BCA

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Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),

T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

Android mini project for final year BSC

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Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),

T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

Android mini projects for final year students

For more details Contact:

Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),

T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

Mechanical projects for final year B.Tech

For more details Contact:

Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),

T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

Android projects for final year MCA

For more details Contact:

Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),

T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

Internship projects for final MCA

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Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),

T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

mini projects for final year BCA...

For more details Contact:

Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),

T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

Mini projects for final year BSC computer science

For more details Contact:

Gagner Technologies,

No: 1, South Dhandapani Street (Opp to T.Nagar bus depot),

T.Nagar, Ch=17.

Contact: 8680939422, 044 24320908. 9962221452

Mail Id: gagnertechnologies@yahoo.in

Friday, 23 May 2014

You Are the Only Possible Oracle: Effective Test Selection for End Users of Interactive Machine Learning Systems

How do you test a program when only a single user, with no expertise in software testing, is able to determine if the program is performing correctly? Such programs are common today in the form of machine-learned classifiers. We consider the problem of testing this common kind of machine-generated program when the only oracle is an end user: e.g., only you can determine if your email is properly filed. We present test selection methods that provide very good failure rates even for small test suites, and show that these methods work in both large-scale random experiments using a “gold standard” and in studies with real users. Our methods are inexpensive and largely algorithm-independent. Key to our methods is an exploitation of properties of classifiers that is not possible in traditional software testing. Our results suggest that it is plausible for time-pressured end users to interactively detect failures—even very hard-to-find failures—without wading through a large number of successful (and thus less useful) tests. We additionally show that some methods are able to find the arguably most difficult-to-detect faults of classifiers: cases where machine learning algorithms have high confidence in an incorrect result.

Verifying Protocol Conformance Using Software Model Checking for the Model-Driven Development of Embedded Systems

To facilitate modular development, the use of state machines has been proposed to specify the protocol (i.e., the sequence of messages) that each port of a component can engage in. The protocol conformance checking problem consists of determining whether the actual behavior of a component conforms to the protocol specifications on its ports. In this paper, we consider this problem in the context of the model-driven development (MDD) of embedded systems based on UML 2, in which UML 2 state machines are used to specify component behavior. We provide a definition of conformance which slightly extends those found in the literature and reduce the conformance check to a state space exploration. We describe a tool implementing the approach using the Java PathFinder software model checker and the MDD tool IBM Rational RoseRT, discuss its application to three case studies, and show how the tool repeatedly allowed us to find unexpected conformance errors with encouraging performance. We conclude that the approach is promising for supporting the modular development of embedded components in the context of industrial applications of MDD.

Variability in Software Systems a Systematic Literature Review

Context: Variability (i.e., the ability of software systems or artifacts to be adjusted for different contexts) became a key property of many systems. Objective: We analyze existing research on variability in software systems. We investigate variability handling in major software engineering phases (e.g., requirements engineering, architecting). Method: We performed a systematic literature review. A manual search covered 13 premium software engineering journals and 18 premium conferences, resulting in 15,430 papers searched and 196 papers considered for analysis. To improve reliability and to increase reproducibility, we complemented the manual search with a targeted automated search. Results: Software quality attributes have not received much attention in the context of variability. Variability is studied in all software engineering phases, but testing is underrepresented. Data to motivate the applicability of current approaches are often insufficient; research designs are vaguely described. Conclusions: Based on our findings we propose dimensions of variability in software engineering. This empirically grounded classification provides a step towards a unifying, integrated perspective of variability in software systems, spanning across disparate or loosely coupled research themes in the software engineering community. Finally, we provide recommendations to bridge the gap between research and practice and point to opportunities for future research

Usability through Software Design

Over the past two decades, the HCI community has proposed specific features that software applications should include to overcome some of the most common usability problems. However, incorporating such usability features into software applications may not be a straightforward process for software developers who have not been trained in usability (i.e., determining when, how, and why usability features should been considered). We have defined a set of usability guidelines for software development to help software engineers incorporate particular usability features into their applications. In this paper, we focus on the software design artifacts provided by the guidelines. We detail the structure of the proposed design artifacts and how they should be used according to the software development process and software architecture used in each application. We have tested our guidelines in an academic setting. Preliminary validation shows that the use of the guidelines reduces development time, improves the quality of the resulting designs, and significantly decreases the perceived complexity of the usability features from the developers' perspective

Task Environment Complexity, Global Team Dispersion, Process Capabilities, and Coordination in Software Development

Software development teams are increasingly global. Team members are separated by multiple boundaries such as geographic location, time zone, culture, and organization, presenting substantial coordination challenges. Global software development becomes even more challenging when user requirements change dynamically. However, little empirical research has investigated how team dispersion across multiple boundaries and user requirements dynamism, which collectively increase task environment complexity, influence team coordination and software development success in the global context. Further, we have a limited understanding of how software process capabilities such as rigor, standardization, agility, and customizability mitigate the negative effects of global team dispersion and user requirements dynamism. To address these important issues, we test a set of relevant hypotheses using field survey data obtained from both project managers and stakeholders. Our results show that global team dispersion and user requirements dynamism have a negative effect on coordination effectiveness. We find that the negative effect of global team dispersion on coordination effectiveness decreases as process standardization increases and that the negative effect of user requirements dynamism on coordination effectiveness decreases as process agility increases. We find that coordination effectiveness has a positive effect on global software development success in terms of both process and product aspects.

Synthesizing Multithreaded Code from Real-Time Object-Oriented Models via Schedulability Aware Thread Derivation

One of the major difficulties in developing embedded systems with object-oriented modeling is to translate a designed model into code that satisfies required real-time performance. This paper proposes Scenario-based Implementation Synthesis Architecture with timing guarantee (SISAtime) that addresses these difficulties. The problems that SISAtime must solve are how to synthesize multithreaded-code from a real-time object-oriented model; and how to design supporting development tools and run-time system architecture while ensuring that the scenarios in the system have minimal response times and the code satisfies the given timing constraints with a minimal number of threads. SISAtime provides a new scheduling algorithm which minimizes scenario response times. SISAtime also provides a new thread derivation method that derives tasks and maps tasks to threads while automatically assigning task scheduling attributes. We have fully implemented SISAtime by extending the RoseRT development tool that uses UML 2.0 as a modeling language, and we applied it to an existing industrial Private Branch Exchange system. The performance evaluation results show that the response times, context switches, and the number of threads of the system with SISAtime were reduced by 21.6%, 33.2%, and 65.2 %, respectively, compared to the system with the best known existing thread derivation method

Symbolic Crosschecking of Data-Parallel Floating-Point Code

We present a symbolic execution-based technique for crosschecking programs accelerated using SIMD or OpenCL against an un accelerated version, as well as a technique for detecting data races in OpenCL programs. Our techniques are implemented in KLEE-CL, a tool based on the symbolic execution engine KLEE that supports symbolic reasoning on the equivalence between expressions involving both integer and floating-point operations. While the current generation of constraint solvers provides effective support for integer arithmetic, the situation is different for floating-point arithmetic, due to the complexity inherent in such computations. The key insight behind our approach is that floating-point values are only reliably equal if they are essentially built by the same operations. This allows us to use an algorithm based on symbolic expression matching augmented with canonicalization rules to determine path equivalence. Under symbolic execution, we have to verify equivalence along every feasible control-flow path. We reduce the branching factor of this process by aggressively merging conditionals, if-converting branches into select operations via an aggressive phi-node folding transformation. To support the Intel Streaming SIMD Extension (SSE) instruction set, we lower SSE instructions to equivalent generic vector operations, which in turn are interpreted in terms of primitive integer and floating point operations. To support OpenCL programs, we symbolically model the OpenCL environment using an OpenCL runtime library targeted to symbolic execution. We detect data races by keeping track of all memory accesses using a memory log, and reporting a race whenever we detect that two accesses conflict. By representing the memory log symbolically, we are also able to detect races associated with symbolically-indexed accesses of memory objects. We used KLEE-CL to prove the bounded equivalence between scalar and data-parallel versions of floating-point programs and find a number of issues in a variety of open source projects that use SSE and OpenCL, including mismatches between implementations, memory errors, race conditions and a compiler bug

Supporting the Combined Selection of Model-based Testing Techniques

The technical literature on Model-Based Testing (MBT) offers us several techniques with different characteristics and goals. Contemporary software projects usually need to make use of different software testing techniques. However, a lack of empirical information regarding their scalability and effectiveness is observed. It makes their application difficult in real projects, increasing the technical difficulties to combine two or more MBT techniques for the same software project. In addition, current software testing selection approaches offer limited support for the combined selection of techniques. Therefore, this paper describes the conception and evaluation of an approach aimed at supporting the combined selection of MBT techniques for software projects. It consists of an evidence-based body of knowledge with 219 MBT techniques and their corresponding characteristics and a selection process that provides indicators on the level of adequacy (impact indicator) amongst MBT techniques and software projects characteristics. Results from the data analysis indicate it contributes to improve the effectiveness and efficiency of the selection process when compared to another selection approach available in the technical literature. Aiming at facilitating its use, a computerized infrastructure, evaluated into an industrial context and evolved to implement all the facilities needed to support such selection approach, is presented

Supporting Domain Analysis through Mining and Recommending Features from Online Product Listings

Domain analysis is a labor-intensive task in which related software systems are analyzed to discover their common and variable parts. Many software projects include extensive domain analysis activities, intended to jump start the requirements process through identifying potential features. In this paper, we present a recommend-er system that is designed to reduce the human effort of performing domain analysis. Our approach relies on data mining techniques to discover common features across products as well as relationships among those features. We use a novel incremental diffusive algorithm to extract features from online product descriptions, and then employ association rule mining and the (k)-nearest neighbor machine learning method to make feature recommendations during the domain analysis process. Our feature mining and feature recommendation algorithms are quantitatively evaluated and the results are presented. Also, the performance of the recommender system is illustrated and evaluated within the context of a case study for an enterprise-level collaborative software suite. The results clearly highlight the benefits and limitations of our approach, as well as the necessary preconditions for its success.

Specification and Verification of Normative Texts using C-O Diagrams

C-O Diagrams have been introduced as a means to have a more visual representation of normative texts and electronic contracts, where it is possible to represent the obligations, permissions and prohibitions of the different signatories, as well as the penalties resulting from non-fulfillment of their obligations and prohibitions. In such diagrams we are also able to represent absolute and relative timing constraints. In this paper we present a formal semantics for C-O Diagrams based on timed automata extended with information regarding the satisfaction and violation of clauses in order to represent different deontic modalities. As a proof of concept, we apply our approach to two different case studies, where the method presented here has successfully identified problems in the specification.

REPENT Analyzing the Nature of Identifier Renaming

Source code lexicon plays a paramount role in software quality poor lexicon can lead to poor comprehensibility and even increase software fault-proneness. For this reason, renaming a program entity, i.e., altering the entity identifier, is an important activity during software evolution. Developers rename when they feel that the name of an entity is not (anymore) consistent with its functionality, or when such a name may be misleading. A survey that we performed with 71 developers suggests that 39% perform renaming from a few times per week to almost every day and that 92% of the participants consider that renaming is not straightforward. However, despite the cost that is associated with renaming, renaming are seldom if ever documented—for example, less than 1% of the renaming in the five programs that we studied. This explains why participants largely agree on the usefulness of automatically documenting renaming. In this paper we propose REPENT (REANAMING PROGRAM ENTITIES), an approach to automatically document—detect and classify—identifier renaming in source code. REPENT detects renaming based on a combination of source code differencing and data flow analyses. Using a set of natural language tools, REPENT classifies renaming into the different dimensions of a taxonomy that we defined. Using the documented renaming, developers will be able to, for example, look up methods that are part of the public API (as they impact client applications), or look for inconsistencies between the name and the implementation of an entity that underwent a high risk renaming (e.g., towards the opposite meaning). We evaluate the accuracy and completeness of REPENT on the evolution history of five open-source Java programs. The study indicates a precision of 88% and a recall of 92%. In addition, we report an exploratory study investigating and discussing how identifiers are renamed in the five programs, according to our taxonomy

On the Asymptotic Behavior of Adaptive Testing Strategy for Software Reliability Assessment

In software reliability assessment, one problem of interest is how to minimize the variance of reliability estimator, which is often considered as an optimization goal. The basic idea is that an estimator with lower variance makes the estimates more predictable and accurate. Adaptive Testing (AT) is an online testing strategy, which can be adopted to minimize the variance of software reliability estimator. In order to reduce the computational overhead of decision-making, the implemented AT strategy in practice deviates from its theoretical design that guarantees AT’s local optimality. This work aims to investigate the asymptotic behavior of AT to improve its global performance without losing the local optimality. To this end, a new AT strategy named Adaptive Testing with Gradient Descent method (AT-GD) is proposed. Theoretical analysis indicates that ATGD, a locally optimal testing strategy, converges to the globally optimal solution as the assessment process proceeds. Simulation and experiments are set up to validate AT-GD’s effectiveness and efficiency. Besides, sensitivity analysis of AT-GD is also conducted in this study.

Modular Software Model Checking for Distributed Systems

Distributed systems are complex, being usually composed of several subsystems running in parallel. Concurrent execution and inter-process communication in these systems are prone to errors that are difficult to detect by traditional testing, which does not cover every possible program execution. Unlike testing, model checking can detect such faults in a concurrent system by exploring every possible state of the system. However, most model-checking techniques require that a system be described in a modeling language. Although this simplifies verification, faults may be introduced in the implementation. Recently, some model checkers verify program code at runtime but tend to be limited to stand-alone programs. This article proposes cache-based model checking, which relaxes this limitation to some extent by verifying one process at a time and running other processes in another execution environment. This approach has been implemented as an extension of Java PathFinder, a Java model checker. It is a scalable and promising technique to handle distributed systems. To support a larger class of distributed systems, a check pointing tool is also integrated into the verification system. Experimental results on various distributed systems show the capability and scalability of cache-based model checking.

Methodbook: Recommending Move Method Refactoring via Relational Topic Models

During software maintenance and evolution the internal structure of the software system undergoes continuous changes. These modifications drift the source code away from its original design, thus deteriorating its quality, including cohesion and coupling of classes. Several refactoring methods have been proposed to overcome this problem. In this paper we propose a novel technique to identify Move Method refactoring opportunities and remove the Feature Envy bad smell from source code. Our approach, coined as Methodbook, is based on Relational Topic Models (RTM), a probabilistic technique for representing and modeling topics, documents (in our case methods) and known relationships among these. Methodbook uses RTM to analyze both structural and textual information gleaned from software to better support move method refactoring. We evaluated Methodbook in two case studies. The first study has been executed on six software systems to analyze if the move method operations suggested by Methodbook help to improve the design quality of the systems as captured by quality metrics. The second study has been conducted with eighty developers that evaluated the refactoring recommendations produced by Methodbook. The achieved results indicate that Methodbook provides accurate and meaningful recommendations for move method refactoring operations

iTree: Efficiently Discovering High-Coverage Configurations Using Interaction Trees

Modern software systems are increasingly configurable. While this has many benefits, it also makes some software engineering tasks, such as software testing, much harder. This is because, in theory, unique errors could be hiding in any configuration, and, therefore, every configuration may need to undergo expensive testing. As this is generally infeasible, developers need cost-effective technique for selecting which specific configurations they will test. One popular selection approach is combinatorial interaction testing (CIT), where the developer selects a strength $t$ and then computes a covering array (a set of configurations) in which all $t$-way combinations of configuration option settings appear at least once. In prior work, we demonstrated several limitations of the CIT approach. In particular, we found that a given system’s effective configuration space—the minimal set of configurations needed to achieve a specific goal—could comprise only a tiny subset of the system’s full configuration space. We also found that effective configuration space may not be well approximated by $t$-way covering arrays. Based on these insights we have developed an algorithm called interaction tree discovery (iTree). iTree is an iterative learning algorithm that efficiently searches for a small set of configurations that closely approximates a system’s effective configuration space. On the each iteration in iTree tests the system on a small sample of carefully chosen configurations, monitors the system’s behaviors, and then applies machine learning techniques to discover which combinations of option settings are potentially responsible for any newly observed behaviors. This information is used in the next iteration to pick a new sample of configurations that are likely to reveal further new behaviors. In prior work, we presented an initial version of iTree and performed an initial evaluation with promising results. This paper presents an improved iTree algorithm in greater detail. The key improvements are based on our use of composite proto-interactions a construct that improves iTree’s ability to correctly learn key configuration option combinations, which in turn significantly improves iTree’s running time, without sacrificing effectiveness. Finally, the paper presents a detailed evaluation of the improved iTree algorithm by comparing the coverage it achieves versus that of covering arrays and randomly generated configuration sets, including a significantly expanded scalability evaluation with the $sim$1M-LOC MySQL. Our results strongly suggest that the improved iTree algorithm is highly scalable and can identify a high-coverage test set of configurations more effectively than existing methods.