Residual barriers for utilization of maternal and child health services: community perceptions from rural Pakistan.

Low utilization of maternal and child care services in rural areas has constrained Pakistan from meeting targets of Millennium Development Goals (MDGs) 4 and 5. This study explores community barriers in accessing Maternal and Child Health (MCH) services in ten remote rural districts of Pakistan. It further presents how the barriers differ across a range of MCH services, and also whether the presence of Community Health Workers (CHWs) reduces client barriers. Qualitative methods were used involving altogether sixty focus group discussions with mothers, their spouses and community health workers. Low awareness, formidable distances, expense, and poorly functional services were the main barriers reported, while cultural and religious restrictions were lesser reported. For preventive services including antenatal care (ANC), facility deliveries, postnatal care (PNC), childhood immunization and family planning, the main barrier was low awareness. Conversely, formidable distances and poorly functional services were the main reported constraints in the event of maternal complications and acute child illnesses. The study also found that clients residing in areas served by CHWs had better awareness only of ANC and family planning, while other MCH services were overlooked by the health worker program. The paper highlights that traditional policy emphasis on health facility infrastructure expansion is not likely to address poor utilization rates in remote rural areas. Preventive MCH services require concerted attention to building community awareness, task shifting from facility to community for services provision, and re-energization of CHW program. For maternal and child emergencies there is strong community demand to utilize health facilities, but this will require catalytic support for transport networks and functional health care centers

Alternating GUI Test Generation and Execution

Users of today’s software perform tasks by interacting with a graphical user interface (GUI) front-end via se-quences of input events. Due to the flexibility offered by most GUIs, the number of event sequences grows exponen-tially with length. One ubiquitous challenge of GUI test-ing is to selectively generate those sequences that lead to potentially problematic states. This paper presents ALT, a new technique that generates GUI test cases in batches, by leveraging GUI run-time information from a previously run batch to obtain the next batch. Each successive batch con-sists of “longer ” test cases that expand the state space to be explored, yet prune the “unimportant ” states. The “alter-nating ” nature of ALT allows it to enhance the next batch by leveraging certain relationships between GUI events (e.g., one enables the other, one alters the other’s execution) that are revealed only at run-time and non-trivial to infer stati-cally. An empirical study on four fielded GUI-based appli-cations demonstrates that ALT is successful at identifying complex failure-causing interactions between GUI events.

Automated gui testing guided by usage profiles

Most software developed in recent years has a graphical user interface (GUI). The only way for the end-user to interact with the software application is through the GUI. Hence, acceptance and system testing of the software requires GUI testing. This paper presents a new technique for testing of GUI applications. Information on the actual usage of the application, in the form of “usage profiles, ” is used to ensure that a new version of the application will function correctly. Usage profiles, sequences of events that end-users execute on a GUI, are used to develop a probabilistic usage model of the application. An algorithm uses the model to generate test cases that represent events the user is most likely to execute. Reverse engineering methods are used to extract the underlying structure of the application. An em-pirical study on four open source GUI applications reveals that test suites generated from the probabilistic model are 0.2-22 % of the size of test suites produced directly from us-age profiles. Furthermore, the test suites generated from the model detect more faults per test case than those de-tected directly from the usage profiles, and detect faults not detected by the original profiles

Covering array sampling of input event sequences for automated gui testing

This paper describes a new automated technique to generate test cases for GUIs by using covering arrays (CAs). The key motivation is to generate long GUI event sequences that are systematically sampled at a particular coverage strength. CAs, to date, have not been effectively used in sampling event driven systems such as GUIs which maintain state. We leverage a “stateless ” abstraction of GUIs that allows us to use CAs. Once the CAs have been generated, we reuse the abstractions to reinsert ordering relationships between GUI events, thereby creating executable test cases. A feasi-bility study on a well-studied GUI-based application shows that the new technique is able to detect a large number of previously undetected faults

Using a goal-driven approach to generate test cases for GUIs

The widespread use of GUIs for interacting with soft-ware is leading to the construction of more and more complex GUIs. With the growing complexity comes challenges in testing the correctness of a GUI and the underlying software. We present a new technique to au-tomatically generate test cases for GUIs that exploits planning, a well developed and used technique in ar-tificial intelligence. Given a set of operators, an initial state and a goal state, a planner produces a sequence of the operators that will change the initial state to the goal state. Our test case generation technique first ana-lyzes a GUI and derives hierarchical planning operators from the actions in the GUI. The test designer deter-mines the preconditions and effects of the hierarchical operators, which are then input into a planning system. With the knowledge of the GUI and the way in which the user will interact with the GUI, the test designer creates sets of initial and goal states. Given these ini-tial and final states of the GUI, a hierarchical planner produces plans, or a set of test cases, that enable the goal state to be reached. Our technique has the ad-ditional benefit of putting verification commands into the test cases automatically. We implemented our tech-nique by developing the GUI analyzer and extending a planner. We generated test cases for Microsoft’s Word-Pad to demonstrate the viability and practicality of the approach