Nishith Goel

Early quality prediction: a case study in telecommunications

Predicting the quality of modules lets developers focus on potential problems and make improvements earlier in development, when it is more cost-effective. The authors applied discriminant analysis to identify fault-prone modules in a large telecommunications system prior to testing.

Detection of software modules with high debug code churn in a very large legacy system

Society has become so dependent on reliable telecommunications, that failures can risk loss of emergency service, business disruptions, or isolation from friends. Consequently, telecommunications software is required to have high reliability. Many previous studies define the classification fault prone in terms of fault counts. This study defines fault prone as exceeding a threshold of debug code churn, defined as the number of lines added or changed due to bug fixes. Previous studies have characterized reuse history with simple categories. This study quantified new functionality with lines of code. The paper analyzes two consecutive releases of a large legacy software system for telecommunications. We applied discriminant analysis to identify fault prone modules based on 16 static software product metrics and the amount of code changed during development. Modules from one release were used as a fit data set and modules from the subsequent release were used as a test data set. In contrast, comparable prior studies of legacy systems split the data to simulate two releases. We validated the model with a realistic simulation of utilization of the fitted model with the test data set. Model results could be used to give extra attention to fault prone modules and thus, reduce the risk of unexpected problems.

Beaconless position based routing with guaranteed delivery for wireless ad-hoc and sensor networks

Existing position-based routing algorithms, where packets are forwarded in the geographic direction of the destination, normally require that the forwarding node knows the positions of all neighbors in its transmission range. This information on direct neighbors is gained by observing beacon messages that each node sends out periodically. Several beaconless greedy routing schemes have been proposed recently. However, none of the existing beaconless schemes guarantee the delivery of packets. Moreover, they incur communication overhead by sending excessive control messages or by broadcasting data packets. In this paper, we describe how existing localized position based routing schemes that guarantee delivery can be made beaconless, while preserving the same routes. In our guaranteed delivery beaconless routing scheme, the next hop is selected through the use of control RTS/CTS messages and biased timeouts. In greedy mode, neighbor closest to destination responds first. In recovery mode, nodes closer to the source will select shorter timeouts, so that other neighbors, overhearing CTS packets, can eliminate their own CTS packets if they realize that their link to the source is not part of Gabriel graph. Nodes also cancel their packets after receiving data message sent by source to the selected neighbor. We analyze the behavior of our scheme on our simulation environment assuming ideal MAC, following GOAFR+ and GFG routing schemes. Our results demonstrate low communication overhead in addition to guaranteed delivery.

ABSRP- A Service Discovery Approach for Vehicular Ad Hoc Networks

A vehicular ad hoc network (VANET) is a network of intelligent vehicles that communicate with other vehicles in the network. The main objective of VANET is to provide comfort and safety for passengers. In addition, various transaction based services, such as information about gas prices, restaurant menu, and discount sale, can be provided to drivers. In order to make these services available, there is a need for an efficient service discovery protocol. In this paper, we propose a new protocol called Address Based Service Resolution Protocol (ABSRP) to discover services in vehicular ad-hoc networks. As most of the transaction based services are provided by roadside units, we exploit their presence to perform service discovery. We utilize the unique address assigned to each service provider in order to discover a route to that service provider. Our technique proactively distributes the service providers address along with its servicing capabilities to other roadside units within a particular area. Each roadside unit will then utilize this information to service the request placed by the vehicles. If the service provider (destination node) is not reachable over the vehicular network, we propose to use a backbone network to service requests. Our approach is independent of the network layer routing protocol. We have evaluated the performance of our approach by using the Qualnet simulation tool.

Detection of fault-prone program modules in a very large telecommunications system

Telecommunications software is known for its high reliability. Society has become so accustomed to reliable telecommunications, that failures can cause major disruptions. This is an experience report on application of discriminant analysis based on 20 static software product metrics, to identify fault prone modules in a large telecommunications system, so that reliability may be improved. We analyzed a sample of 2000 modules representing about 1.3 million lines of code, drawn from a much larger system. Sample modules were randomly divided into a fit data set and a test data set. We simulated utilization of the fitted model with the test data set. We found that identifying new modules and changed modules mere significant components of the discriminant model, and improved its performance. The results demonstrate that data on module reuse is a valuable input to quality models and that discriminant analysis can be a useful tool in early identification of fault prone software modules in large telecommunications systems. Model results could be used to identify those modules that would probably benefit from extra attention, and thus, reduce the risk of unexpected problems with those modules.

The impact of software evolution and reuse on software quality

This paper presents a case study of a software project in the maintenance phase. The case study was based on a sample of modules, representing about 1.3 million lines of code, from a very large telecommunications system. Software quality models were developed to predict the number of faults expected from the coding through operations phases. Since modules from the prior release were often reused to develop a new release, one model incorporated reuse data as additional independent variables. We compare this models performance to a similar model without reuse data.Software quality models often have product metrics as the only input data for predicting quality. There is an implicit assumption that all the modules have had a similar development history, so that product attributes are the primary drivers of different quality levels. Reuse of software as components and software evolution do not fit this assumption very well, and consequently, traditional models for such environments may not have adequate accuracy. Focusing on the software maintenance phase, this study demonstrated that reuse data can significantly improve the predictive accuracy of software quality models.

Environmental impact on optimization analysis for standalone and hybrid power generating units

An algorithm and optimization analysis results for standalone (wind and solar) and hybrid renewable power sources is presented. A generic iteration approach considering dual criteria, namely power reliability and minimum cost of energy is considered for the analysis. Yearlong environmental data (wind speed and solar insolation) is used to test and validate the model. The complimentary nature of the two energy sources is outlined. The cumulative day based analysis may be a necessary approach for unit size analysis, but it does not warranty for full power reliability. Hourly analysis provides 100 percent reliability, but involves large number of power generating units.

A Framework for Automatic Resource Provisioning for Private Clouds

A private cloud is maintained by an enterprise forits internal use. In such a scenario instead of buying the resources the enterprise can acquire the resources from a public cloud such as the ones provided by Amazon and Microsoft. On conventional systems rigorous analysis of the system and its workload is performed for determining the appropriate number of resources to be deployed on the private cloud. This paper presents a middleware framework that avoids this step of a priori capacity analysis and allows such private cloud owners to provision resources automatically such that a specified grade of service is maintained. The proposed framework performs dynamic resource provisioning that also leads to a reduction of operational cost. Additional resources are acquired during high traffic periods and released during low traffic periods such that the desired grade of service is always maintained. The paper describes the architecture of the framework and the experience gained from a prototype implementation including a preliminary analysis of its performance.

Routing Management Algorithm Based on Spectrum Trading and Spectrum Competition in Cognitive Radio Networks

Traditionally in routing approaches, each node allows a maximum load through the selected route. The existing routing approaches in cognitive radio networks (CRN) do not take into account spectrum trading as well as spectrum competition among licensed users (PUs). This paper introduces a novel routing algorithm that is based on spectrum trading and spectrum competition for cognitive radio networks while supporting different QoS levels for unlicensed users (SUs). The proposed path selection algorithm among different paths is based on user profiles which contain parameters such as SU identification, number of hops, channel identification, neighbor identification, probabilities of idle slots and PU presence. Each node shares its profile with the neighbor PU, which then exchanges its profile with other PUs and decides based on the information received. In spectrum trading phase a PU calculates its price based on the SU requirements. In spectrum competition phase a new coefficient α is defined that controls the price because of competition among PUs and depends on many factors such as the number of primary users, available channels, and duration of the usage. All possible paths are managed and categorized based on the level of QoS requested by SUs and the price offered by the PU.

Power-aware Depth First Search based georouting in ad hoc and sensor wireless networks

Depth First Search (DFS) and position based routing algorithms were proposed in literature. These are localized algorithms that guarantee the delivery for connected ad hoc and sensor wireless networks modeled by arbitrary graphs, including inaccurate location information for a destination node. This paper first optimizes an existing DFS based routing scheme by eliminating from the candidate list neighbors whose messages to other nodes were overheard. We then introduce a new set of localized routing algorithms. The new DFS routing protocol is integrated with power metrics minimizing total power for routing of a message. These DFS Power Progress based algorithms are combinations of known greedy power ad DFS routing algorithms. All algorithms are further enhanced by applying the concept of connected dominating sets, which greatly reduced the search path without impacting significantly the length of effectively constructed path for real tragic. Experiments confirm the efficiency of the new enhanced DFS, power aware and connected dominating set based routing algorithms and ability to guarantee the delivery in arbitrary model due to the DFS routing framework.