Abstract Estimating the number of defects in a software product is an important and challenging problem. A multitude of estimation techniques have been proposed for defect prediction. However, not all techniques are applicable in all cases. The selection of the proper approach to use depends on multiple factors: the features of the approach; the availability of resources; and the goals for using the estimated defect data. In this paper a survey of existing estimation techniques and a decision support approach for selecting the most suitable defect estimation technique for a project, with specific goals, is proposed. The results of the ranking are a clear indication that no estimation technique provides a single, comprehensive solution; the selection must be done according to a given scenario. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper, we propose a socioscope model for social-network and human-behavior analysis based on mobile-phone call-detail records. Because of the diversity and complexity of human social behavior, no one technique will detect every attribute that arises when humans engage in social behaviors. We use multiple probability and statistical methods for quantifying social groups, relationships, and communication patterns and for detecting human-behavior changes. We propose a new index to measure the level of reciprocity between users and their communication partners. This reciprocity index has application in homeland security, detection of unwanted calls (e.g., spam), telecommunication presence, and product marketing. For the validation of our results, we used real-life call logs of 81 users which contain approximately 500 000 h of data on users' location, communication, and device-usage behavior collected over eight months at the Massachusetts Institute of Technology (MIT) by the Reality Mining Project group. Also, call logs of 20 users collected over six months by the University of North Texas (UNT) Network Security team are used. The MIT and UNT data sets contain approximately 5000 callers. The experimental results show that our model is effective.

Summary form only given.The Bayesian Belief Network (BBN) is a very powerful tool for causal relationship modeling and probabilistic reasoning. A BBN has two components. First is its structure a directed acyclic graph (DAG) whose nodes represent random variables and whose arcs represent the dependencies between the variables. The second component is its parameter in the form of Conditional Probability Tables (CPTs).The BBN is widely used in many different areas, excelling itself in Prediction, Risk Analysis, Diagnosis and Decision Support.

Botnets have become the major sources of spamming, which generates massive unwanted traffic on networks. An effective detection mechanism can greatly mitigate the problem. In this paper, we present a novel botnet detection mechanism based on the email "shape" analysis that relies on neither content nor reputation analysis. Shape is our new way of characterizing an email by mimicking human visual inspection. A set of email shapes are derived and then used to generate a botnet signature. Our preliminary results show greater than 80% classification accuracy (without considering email content or reputation analysis). This work investigates the discriminatory power of email shape, for which we believe will be a significant complement to other existing techniques such as a network behavior analysis.

Despite all the advances on techniques to block spam e-mail messages we still receive them on a frequent basis. This is due mainly to the ability of the spammers to modify the message and pass the filters. Therefore a testing technique that could resemble the behavior of spammers would improve the number of scenarios tested and allow filters to be developed based on the potential changes made by the spammers. An approach based on natural language processing (NLP) for the penetration of spam filters is proposed here. Preliminary results using SpamAssassin are provided indicating the feasibility of the proposed approach.

Choosing suitable Requirement Engineering (RE) techniques for a particular project is a challenging and time-consuming task, requiring substantial expertise and efforts. In this paper, an expert system based approach is proposed to help solving this problem. This expert system is capable of modeling and selecting suitable RE techniques for a software project. An on-line questionnaire is created in the first place to collect the expertise available in the community. A new algorithm is proposed to convert the raw data to a training data set suitable for building a complete Bayesian Belief Network (BBN). The resulting BBN is used to build sub-BBNs for RE techniques in six RE phases. The sub-BBNs integrated with a user interface form the expert system. Empirical study shows that the expert system outperforms other predictors in selecting suitable RE techniques. A case study is conducted to show the application of the expert system in the real world.

Online social networking sites (SNSs) has changed our lifestyle and become a main medium of communication among young adults to stay in touch with their friends, to organize events, to make friends, to promote themselves, to date, etc. To create content rich environment, SNSs make their platform available for third-party developers. The developers can build their applications based on users' social graph containing their personal and social information. Unfortunately, any information users posted on their profile can be harvested and used for unethical purposes due to Facebook's lack of application privacy configuration. In this paper we propose a privacy-management system for Facebook applications. The system can take advantage of the correlation between some profile features and network privacy settings, in this way it can automatically configure a users privacy settings. Our preliminary result show promising result.

In this paper we propose a method for combining wavelet denoising and sequential approach for detecting change points on mobile phone based on detailed call records. The Minmax method is used to estimate the thresholds of frequency and call duration for denoising. This work is useful to enhance homeland security, detecting unwanted calls (e.g., spam) and commercial purposes. For validation of our results, we randomly choose actual call logs of 20 users from 100 users collected at MIT by the Reality Mining Project group for a period of 8 months. Simulation data is also used to validate the results. The experimental results show that our model achieves good performance with high accuracy.

Component-based software development techniques are being adopted to rapidly deploy complex, high quality systems. One of its aspects is the selection of components that realize the specified requirements. In addition to the functional requirements, the selection must be done taking into account some non-functional requirements such as performance, reliability, and usability. Hence, data that characterize the non-functional behavior of the components is needed; a test set is needed to collect this data for each component under consideration. This set may be large, which results in a considerable increase in the cost of the development process. Here, a process is proposed to considerably reduce the number of test cases used in the performance evaluation of components. The process is based on sequential curve fittings from an incremental number of test cases until a minimal pre-specified residual error is achieved. The incremental selection of test cases is done in two different ways: randomly and adaptively. The accuracy and performance of the proposed approach are dependent on the values of the desired residual error. The smaller the residual error, the higher the accuracy. However, performance has an opposite behavior. The smaller the error, the larger the number of test cases needed. The results from experiments with image compression components are a clear indication that a reduction in the number of test cases can be achieved while maintaining reasonable accuracy when using the proposed approach.

Abstract Risk management refers to the process of making decisions that minimize the effects of vulnerabilities on the network hosts. This can be a difficult task in the context of high‐exploit probability and the difficult to identify new exploits and vulnerabilities. For many years, security engineers have performed risk analysis using economic models for the design and operation of risk‐prone, technological systems using attack profiles. Based on the type of attacker identified, security administrators can formulate effective risk management policies for a network. We hypothesize that sequence of network actions by an attacker depends on the social behavior (e.g., skill level, tenacity, financial ability). We extended this and formulated a mechanism to estimate the risk level of critical resources that may be compromised based on attacker behavior. This estimation is accomplished using behavior based attack graphs representing all the possible attack paths to all the critical resources. The risk level is computed based on these graphs and are used as a measure of the vulnerability of the resource and forming an effective basis for a system administrator to perform suitable changes to network configuration. Copyright © 2008 John Wiley & Sons, Ltd.

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Email spam is the most effective form of online advertising. Unlike telephone marketing, email spamming does not require huge human or financial resources investment. Most existing spam filtering techniques concentrate on the emails’ content. However, most spammers obfuscate their emails’ content to circumvent content-based spam filters. An integrated solution for restricting spam emails is needed as content analyses alone might not provide a solution for filtering unsolicited emails. Here we present a new method for isolating unsolicited emails. Though spammers obfuscate their emails’ content, they do not have access to all the fields in the email header. Our classification method is based on the path an email traverses instead of content. Overall, our classifier produced fewer false positives when compared to current filters such as SpamAssassin. We achieved a precision of 98.65% which compares well with the precisions achieved by SPF, DNSRBL blacklists.

In our everyday life, we communicate with many people such as family, friends, neighbors, and colleagues. We communicate with them using different communication media such as email, telephone calls, and face-to-face interactions. While email is not real-time and face-to-face communications require geographic proximity, voice and video communications are preferred over other modes of communication. However, real-time voice/video calls may create nuisance to the receiver. In this article, we describe a mathematical model for computing nuisance level of incoming voice/video calls. We computed the closeness and nuisance level using the calling patterns between the caller and the callee. To validate the nuisance model, we collected cell phone call records of real-life people at our university and computed the nuisance value for all voice calls. We validated the nuisance levels using the feedback from those real-life people. Such a nuisance model is useful for predicting unwanted voice and video sessions in an IP communication network.

An accurate prediction of the number of defects in a software product during system testing contributes not only to the management of the system testing process but also to the estimation of the product's required maintenance. Here, a new approach called ED3M is presented that computes an estimate of the total number of defects in an ongoing testing process. ED3M is based on estimation theory. Unlike many existing approaches the technique presented here does not depend on historical data from previous projects or any assumptions about the requirements and/or testers' productivity. It is a completely automated approach that relies only on the data collected during an ongoing testing process. This is a key advantage of the ED3M approach, as it makes it widely applicable in different testing environments. Here, the ED3M approach has been evaluated using five data sets from large industrial projects and two data sets from the literature. In addition, a performance analysis has been conducted using simulated data sets to explore its behavior using different models for the input data. The results are very promising; they indicate the ED3M approach provides accurate estimates with as fast or better convergence time in comparison to well-known alternative techniques, while only using defect data as the input.

Autonomous adaptive systems (AAS) have been proposed as a solution to effectively (re)design software so that it can respond to changes in execution environments, without human intervention. In the software engineering community, alternative approaches to the design of AAS have been proposed including solutions based on component technology, design patterns, and resource allocation techniques. A key limitation of the currently available approaches is that they detect constraint violations, but they do not support the prediction of constraint violations. In this work we propose an architectural framework for the design and analysis of autonomous adaptive systems, hereafter referred to as KAROO, which provides a key, new contribution: the capability to predict when a system needs to adapt itself. The results of extensive experimental evaluation of a KAROO-based system are excellent: 100% of the violations are predicted; the system is able to avoid the violations by adapting itself almost 98% of the time. The framework is a novel integration of control-theory-based adaptation, multi-criteria decision making and component-based software engineering techniques.

In a computer network, network security is accomplished using elements such as firewalls, hosts, servers, routers, intrusion detection systems, and honey pots. These network elements need to know the nature or anomaly of the worm a priori to detect the attack. Modern viruses such as Code Red, Sapphire, and Nimda spread quickly. Therefore, it is impractical if not impossible for human mediated responses to these fast-spreading viruses. Several epidemic studies show that automatic tracking of resource usage and control provides an effective method to contain the damage. In this paper, we propose a novel security architecture based on the control system theory. In particular, we describe a state-space feedback control model that detects and control the spread of these viruses or worms by measuring the velocity of the number of new connections an infected host makes. The mechanism's objective is to slow down a worm's spreading velocity by controlling (delaying) the number of new connections made by an infected host. A proportional and integral (PI) controller is used for a continuous control of the feedback loop. The approach proposed here has been verified in a laboratory setup, and we were able to contain the infection so that it affected less than 5 percent of the hosts. We have also implemented a protocol for exchanging control-specific information between the network elements. The results from the simulation and experimental setup combined with the sensitivity analysis demonstrate the applicability and accuracy of the approach.

Load and stress testing are important to guarantee the system is able to support specified load conditions as well as properly recover from the excess use of resources. The generation of test cases to achieve levels of load and stress is a demanding task. Here we propose an approach for the automatic generation of test cases to achieve specified levels of load and stress for a combination of resources. The technique is based on the use of a PID controller to drive the inputs to the desired levels. The preliminary results are a good indication of the potential of the approach

The application of the CDM model has proven to be effective and accurate. The piecewise approximation used relies on the proper identification of re-calibration points. These points are identified based on the observation of the testing process. However, not all change points are easily identifiable. In this paper, we present a new technique for the automatic identification of change points which increases the applicability and accuracy of 'the CDM model. In addition, identification of change points can also be used to increase the understanding of the general behavior of a testing process by providing an expected signature for future projects

Agile processes can provide early defect information on all the stages of the development process. The use of a feedback control model to regulate the process based on this information is proposed here. The model was originally designed to regulate the testing process and has proven to be effective. Additionally, an ODC filter is proposed to properly interconnect the models associated with each development phase. The approach appears to be a reasonable solution for the control and prediction of agile processes.

The generally accepted calculation to measure the productivity of a software engineer is based on economic theory and is borrowed from traditional product manufacturing environments. Managers often measure the productivity of a worker to determine merit-based raises or to provide feedback to workers with poor productivity. The assumption is that this calculation of a worker's productivity is directly proportional to a worker's value to the company. The motivation for the approach proposed here is that such relationship may not be algebraically captured with respect to the productivity of software engineers. To better capture the productivity of a software engineer and his value to a company, the productivity problem is reformulated here as a pattern recognition problem and solved using clustering. By defining a general productivity operator, clustering has been used to map the domain of the productivity operator to a range of productivity classes. This new approach has been successfully applied to randomly generated project data and actual project data from the NASA SATC software metric database.

We describe two case studies to investigate the application of a state variable model to control the system test phase of software products. The model consists of two components: a feedback control portion and a model parameter estimation portion. The focus in this study is on the assessment of the goodness of the estimates and predictions of the model parameters and their utility in the management of the system test phase. Two large network management applications developed and tested at Sun Microsystems served as the subjects in these studies. Unlike the release of products based on marketing or deadline pressure, estimates of the number of residual defects are used to control the quality of the product being released. The estimates of the number of defects in the application when the test phase began and at the current checkpoint are obtained. In addition a prediction is made regarding the reduction in the number of remaining defects over the remaining period. The estimates and predictions assist the management in planning the test phase and allow inferring the level of customer support needed subsequent to product release. The results of both case studies are satisfactory and, when viewed in light of other studies conducted at Sun Microsystems, show the applicability of the state variable model to the management of the software test process.

Statistical Process Control (SPC) is a powerful tool to control the quality of processes. It assists management personnel in the identification of problems and actions to be taken to bring a process into a stable state. SPC has been applied in various fields, including the Software Development Process. However, some processes are better characterized by factors that exhibit an exponential behavior. The use of such factors for process control limits the application of traditional SPC techniques. The Software Test Process (STP) characterized by the decay in the number of remaining errors, failure intensity, and an increase in code coverage, is one such process.A variant of the traditional SPC technique is proposed. This variant uses logarithmic transformation to allow the statistical control of processes whose dominant behavior is best described by an exponential. An evaluation of the proposed transformation carried out using simulation and a case study from an industrial project, encourages the application of the proposed variant to the STP.

We report on the sensitivity analysis of a state variable model (Model S) proposed earlier. Model S captures the dominant behavior of the system test phase of the software test process. Sensitivity analysis is a mathematical methodology to compute changes in the system behavior due to changes in system parameters or variables. This is particularly important when parameters are calibrated using noisy or small data sets. Nevertheless, by mathematically quantifying the effects of parameter variations on the behavior of the model, and thereby the STP, one can easily and quickly evaluate the effect of such variations on the process performance without having to perform extensive simulations. In all cases studied, model S behaved according to empirical observations which serves to validate the model. It is also shown that sensitivity analysis can suggest structural improvements in a model when the model does not behave as expected.

We report a study to determine the impact of four types of disturbances on the failure intensity of a software product undergoing system test. Hardware failures, discovery of a critical fault, attrition in the test team, are examples of disturbances that will likely affect the convergence of the failure intensity to its desired value. Such disturbances are modeled as impulse, pulse, step, and white noise. Our study examined, in quantitative terms, the impact of such disturbances on the convergence behavior of the failure intensity. Results from this study reveal that the behavior of the state model, proposed elsewhere, is consistent with what one might predict. The model is useful in that it provides a quantitative measure of the delay one can expect when a disturbance occurs.

A novel approach to model the system test phase of the software life cycle is presented. This approach is based on concepts and techniques from control theory and is useful in computing the effort required to reduce the number of errors and the schedule slippage under a changing process environment. Results from these computations are used, and possibly revised, at specific checkpoints in a feedback-control structure to meet the schedule and quality objectives. Two case studies were conducted to study the behavior of the proposed model. One study reported here uses data from a commercial project. The outcome from these two studies suggests that the proposed model might well be the first significant milestone along the road to a formal and practical theory of software process control.

A novel approach for modeling and control of the software test process is presented. The approach is based on the concept of state variables and uses techniques from the well-established field of automatic control theory. An initial model of the software test phase is described and the results of a case study analysis are presented.

A closed-loop feedback control model of the software test process (STP) is described. The model is grounded in the well established theory of automatic control. It offers a formal and novel procedure for using product reliability or failure intensity as a basis for closed loop control of the STP. The reliability or the failure intensity of the product is compared against the desired reliability at each checkpoint and the difference fed back to a controller. The controller uses this difference to compute changes necessary in the process parameters to meet the reliability, or failure intensity objective at the terminal checkpoint (the deadline). The STP continues beyond a checkpoint with a revised set of parameters. This procedure is repeated at each checkpoint until the termination of the STP. The procedure accounts for the possibility of changes (during testing), in reliability or failure intensity objective, the checkpoints, and the parameters that characterize the STP. The effectiveness of this procedure was studied using commercial data available in the public domain and also from the data generated through simulation. In all cases, the use of feedback control produces adequate results allowing the achievement of the objectives.

A model is proposed to assist software test managers in controlling the behavior and progress of the Software Test Process (STP) by allowing them to compare predicted behavior against observed progress made at various checkpoints. The model, whose parameters are based on measured data and process characteristics, generates the predicted behavior. An algorithm for the parameter estimation is set forth. The error between the predicted and desired behavior is used to drive a parametric control algorithm that tells the manager how to correct for schedule deviations.

The paper reports the results of a sensitivity analysis of a state variable model of the Software Test Process (STP). Given a state model of the STP, a sensitivity matrix is calculated using tensor algebra. The sensitivity matrix allows computation of output variations to small perturbations in the model parameters. The results confirm that the model behaves in a manner very similar to what one might expect from a software test process. Results of this analysis also suggest changes and enhancements in the model to improve its accuracy in predicting the behavior of the Software Test Process.