IEEE Projects 2014 For Cse in Software Engineering Java

distributed software product development. We took an action research approach to study the SPI journey of a large multinational

enterprise that adopted a distributed product development strategy. We describe the interventions and action research cycles enacted

over a period of five years in collaboration with the firm, which resulted in a custom SPI framework that catered to both the social and

technical needs of the firm’s distributed teams. Institutionalizing the process maturity framework got stalled initially because the SPI

initiatives were perceived by product line managers as a mechanism for exercising wider controls by the firm’s top management. The

implementation mechanism was subsequently altered to co-opt product line managers, which contributed to a wider adoption of the SPI

framework. Insights that emerge from our analysis of the firm’s SPI journey pertain to the integration of the technical and social views of

software development, preserving process diversity through the use of a multi-tiered, non-blueprint approach to SPI, the linkage

between key process areas and project control modes, and the role of SPI in aiding organizational learning

and, therefore,every configuration may need to undergo expensive testing. As this is generally infeasible, developers need costeffective

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 each iteration 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 _1M-LOC MySQL. Our results strongly suggest that the improved iTree algorithm is highly scalable and can identify a highcoverage

test set of configurations more effectively than existing methods

Multithreaded Programs

potential deadlock localization. It divides lock dependencies into thread specific partitions, consolidates equivalent lock dependencies,

and searches over the set of lock dependency chains without the need to examine any duplicated permutations of the same lock

dependency chains. We validate Magiclock through a suite of real-world, large-scale multithreaded programs. The experimental results

show that Magiclock is significantly more scalable and efficient than existing dynamic detectors in analyzing and detecting potential

deadlocks in execution traces of large-scale multithreaded programs

distributed software product development. We took an action research approach to study the SPI journey of a large multinational

enterprise that adopted a distributed product development strategy. We describe the interventions and action research cycles enacted

over a period of five years in collaboration with the firm, which resulted in a custom SPI framework that catered to both the social and

technical needs of the firm’s distributed teams. Institutionalizing the process maturity framework got stalled initially because the SPI

initiatives were perceived by product line managers as a mechanism for exercising wider controls by the firm’s top management. The

implementation mechanism was subsequently altered to co-opt product line managers, which contributed to a wider adoption of the SPI

framework. Insights that emerge from our analysis of the firm’s SPI journey pertain to the integration of the technical and social views of

software development, preserving process diversity through the use of a multi-tiered, non-blueprint approach to SPI, the linkage

between key process areas and project control modes, and the role of SPI in aiding organizational learning

and, therefore,every configuration may need to undergo expensive testing. As this is generally infeasible, developers need costeffective

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 each iteration 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 _1M-LOC MySQL. Our results strongly suggest that the improved iTree algorithm is highly scalable and can identify a highcoverage

test set of configurations more effectively than existing methods

Multithreaded Programs

potential deadlock localization. It divides lock dependencies into thread specific partitions, consolidates equivalent lock dependencies,

and searches over the set of lock dependency chains without the need to examine any duplicated permutations of the same lock

dependency chains. We validate Magiclock through a suite of real-world, large-scale multithreaded programs. The experimental results

show that Magiclock is significantly more scalable and efficient than existing dynamic detectors in analyzing and detecting potential

deadlocks in execution traces of large-scale multithreaded programs