Software project managers need an accurate assessment of software development efforts to achieve reliable software within development budget and schedule. A single layer neural network (SLP) is reported to predict software development efforts from software quality metrics. Particle swarm optimisation for training, principal component analysis (PCA) for dimension reduction of input features and genetic algorithm for optimising artificial neural network architecture are used. Literature reported datasets are tested and the results are acceptable within the limits. However, SLP_NN without pre-processing with PCA is adequate and in some cases, reduction approach may be dropped.

Regression test selection is a process to select a subset of existing test cases, which are then used with some new test cases for regression testing. Regression testing ensures that the changes made to the system have not affected the existing functionality. To date, there is no adequate technique which can do regression test selection by considering changes in semantics of operations (along with the other syntax and semantics changes) using UML diagrams. Change in semantics of an operation refers to the change in conditional statements, change in independent paths/unique paths, change in control flow and addition or deletion of any content from the existing functionality. In this study, a novel approach is presented which can do this using class, sequence and activity diagrams. The tool compared old and new versions of UML diagrams to categorise test cases into reusable, retestable, obsolete and newly generated category. Activity diagrams are specifically used to test the semantics of operations. The changed operations corresponding to these activity diagrams are also searched in class and sequence diagrams for regression test selection. This study has been validated by comparison with previous study. It is found that the authors’ work provides significant increase in accuracy.

Malicious modification of software continues to raise concerns; thus, many countermeasures against these threats have been proposed such as obfuscation and tamper resistance techniques that can be combined to create tamper-resistant software. The methods for responding to tampering are an important consideration in tamper-resistant software design. Many tamper response mechanisms deliberately cause the application to crash; however, this response has negative impacts on the developers and users. An alternative approach is to detect and fix malicious changes. However, this approach cannot deter an attacker from attempting to continuously modify target software. This study presents robust tamper-resistant software techniques to mitigate tampering and reverse engineering attacks. The tamper-resistant software is based on an existing control flow flattening scheme and includes extensions for tamper detection and flexibility. Furthermore, the authors introduce a dynamic key in their two-stage tamper response scheme that considers both software stability and continuous attack. When tampering is detected, the proposed two-stage tamper response first produces a warning level response such as self-healing or programme termination. Next, if an attacker continuously attempts to bypass the warning level response, a self-destructive tamper response is triggered, as the result of an incorrectly computed dynamic key.